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European Conference on Service-Oriented and Cloud Computing

ESOCC 2022: Advances in Service-Oriented and Cloud Computing pp 83–87 Cite as

Cloud Computing Continuum Research Topics and Challenges. A Multi-source Analysis

  • Juncal Alonso   ORCID: orcid.org/0000-0002-9244-2652 12 ,
  • Leire Orue-Echevarria   ORCID: orcid.org/0000-0002-0648-4689 12 &
  • Enrique Areizaga   ORCID: orcid.org/0000-0002-8084-876X 12  
  • Conference paper
  • First Online: 01 January 2023

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Part of the Communications in Computer and Information Science book series (CCIS,volume 1617)

While the emergence of COVID-19 [ 1 ] has put major cloud service providers around the world to the test, the pandemic has also provided a strong impetus for the adoption and deployment of cloud computing: the transition to a remote workforce, entertainment, e-commerce, and especially remote education have affected the cloud industry and how providers are responding to the sudden and significant increase in demand for cloud solutions and services. Obviously, while highlighting the robustness of the public cloud, the pandemic-induced situation also highlights several important research challenges that need to be addressed.

This paper presents a multi-source based analysis for the identification of cloud computing research challenges as part of the road mapping methodology followed in the HUB4CLOUD project. The analysis consists of an in-depth study of several sources including analysis of the international context, analysis of academic venues, interviews with relevant stakeholders and existing funded projects.

The paper also provides an overview of the main research topics identified and proposes next steps for the utilization of these finding in the development of a Cloud Computing research roadmap.

  • Cloud computing
  • Research topics
  • Multi-source analysis
  • Cloud continuum

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Sayegh, E.: As COVID-19 Pushes Businesses to Their Limit. The Cloud Rises Above, FORBES, 26 March

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HUB4CLOUD. https://www.h-cloud.eu/ict_40-projects/hub4cloud/ . Accessed 03 March 2022

H-cloud. https://www.h-cloud.eu/ . Accessed 03 March 2022

HUB4Cloud consortium. D1.4 Contributing to the European Cloud Computing Strategic Research and Innovation Agenda Q3–2021 (2021)

Ruf, P., Madan, M., Reich, C., Ould-Abdeslam, D.: Demystifying MLOps and presenting a recipe for the selection of open-source tools. Appl. Sci. 11 , 8861 (2021). https://doi.org/10.3390/app11198861

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Juncal Alonso, Leire Orue-Echevarria & Enrique Areizaga

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Christian Zirpins

University of Cádiz, Cádiz, Spain

Guadalupe Ortiz

Zoltan Nochta

Oliver Waldhorst

University of Pisa, Pisa, Italy

Jacopo Soldani

University of Messina, Messina, Italy

Massimo Villari

TU/e – JADS, Eindhoven, The Netherlands

Damian Tamburri

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Alonso, J., Orue-Echevarria, L., Areizaga, E. (2022). Cloud Computing Continuum Research Topics and Challenges. A Multi-source Analysis. In: Zirpins, C., et al. Advances in Service-Oriented and Cloud Computing. ESOCC 2022. Communications in Computer and Information Science, vol 1617. Springer, Cham. https://doi.org/10.1007/978-3-031-23298-5_7

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Top 10 Cloud Computing Research Topics in 2020

Cloud computing has suddenly seen a spike in employment opportunities around the globe with tech giants like Amazon, Google, and Microsoft hiring people for their cloud infrastructure. Before the onset of cloud computing, companies and businesses had to set up their own data centers, allocate resources and other IT professionals thereby increasing the cost. The rapid development of the cloud has led to more flexibility, cost-cutting, and scalability. 

Top-10-Cloud-Computing-Research-Topics-in-2020

The Cloud Computing market its an all-time high with the current market size at USD 371.4 billion and is expected to grow up to USD 832.1 billion by 2025! It’s quickly evolving and gradually realizing its business value along with attracting more and more researchers, scholars, computer scientists, and practitioners. Cloud computing is not a single topic but a composition of various techniques which together constitute the cloud. Below are 10 the most demanded research topics in the field of cloud computing:

1. Big Data

Big data refers to the large amounts of data produced by various programs in a very short duration of time. It is quite cumbersome to store such huge and voluminous amounts of data in company-run data centers. Also, gaining insights from this data becomes a tedious task and takes a lot of time to run and provide results, therefore cloud is the best option. All the data can be pushed onto the cloud without the need for physical storage devices that are to be managed and secured. Also, some popular public clouds provide comprehensive big data platforms to turn data into actionable insights. 

DevOps is an amalgamation of two terms, Development and Operations. It has led to Continuous Delivery, Integration, and Deployment and therefore reducing boundaries between the development team and the operations team. Heavy applications and software need elaborate and complex tech stacks that demand extensive labor to develop and configure which can easily be eliminated by cloud computing. It offers a wide range of tools and technologies to build, test, and deploy applications with a few minutes and a single click. They can be customized as per the client requirements and can be discarded when not in use hence making the process seamless and cost-efficient for development teams.

3. Cloud Cryptography

Data in the cloud is needed to be protected and secured from foreign attacks and breaches. To accomplish this, cryptography in the cloud is a widely used technique to secure data present in the cloud. It allows users and clients to easily and reliably access the shared cloud services since all the data is secured using either the encryption techniques or by using the concept of the private key. It can make the plain text unreadable and limits the view of the data being transferred. Best cloud cryptographic security techniques are the ones that do not compromise the speed of data transfer and provide security without delaying the exchange of sensitive data. 

4. Cloud Load Balancing

It refers to splitting and distributing the incoming load to the server from various sources. It permits companies and organizations to govern and supervise workload demands or application demands by redistributing, reallocating, and administering resources between different computers, networks, or servers. Cloud load balancing encompasses holding the circulation of traffic and demands that exist over the Internet. This reduces the problem of sudden outages, results in an improvement in overall performance, has rare chances of server crashes, and also provides an advanced level of security. Cloud-based servers farms can accomplish more precise scalability and accessibility using the server load balancing mechanism. Due to this, the workload demands can be easily distributed and controlled.

5. Mobile Cloud Computing

It is a mixture of cloud computing, mobile computing, and wireless network to provide services such as seamless and abundant computational resources to mobile users, network operators, and cloud computing professionals. The handheld device is the console and all the processing and data storage takes place outside the physical mobile device. Some advantages of using mobile cloud computing are that there is no need for costly hardware, battery life is longer, extended data storage capacity and processing power improved synchronization of data and high availability due to “store in one place, accessible from anywhere”. The integration and security aspects are taken care of by the backend that enables support to an abundance of access methods. 

6. Green Cloud Computing

The major challenge in the cloud is the utilization of energy-efficient and hence develop economically friendly cloud computing solutions. Data centers that include servers, cables, air conditioners, networks, etc. in large numbers consume a lot of power and release enormous quantities of Carbon Dioxide in the atmosphere. Green Cloud Computing focuses on making virtual data centers and servers to be more environmentally friendly and energy-efficient. Cloud resources often consume so much power and energy leading to a shortage of energy and affecting the global climate. Green cloud computing provides solutions to make such resources more energy efficient and to reduce operational costs. This pivots on power management, virtualization of servers and data centers, recycling vast e-waste, and environmental sustainability. 

7. Edge Computing

It is the advancement and a much more efficient form of Cloud computing with the idea that the data is processed nearer to the source. Edge Computing states that all of the computation will be carried out at the edge of the network itself rather than on a centrally managed platform or the data warehouses. Edge computing distributes various data processing techniques and mechanisms across different positions. This makes the data deliverable to the nearest node and the processing at the edge. This also increases the security of the data since it is closer to the source and eliminates late response time and latency without affecting productivity.

8. Containerization

Containerization in cloud computing is a procedure to obtain operating system virtualization. The user can work with a program and its dependencies utilizing remote resource procedures. The container in cloud computing is used to construct blocks, which aid in producing operational effectiveness, version control, developer productivity, and environmental stability. The infrastructure is upgraded since it provides additional control over the granular activities over the resources. The usage of containers in online services assists storage with cloud computing data security, elasticity, and availability. Containers provide certain advantages such as a steady runtime environment, the ability to run virtually anywhere, and the low overhead compared to virtual machines. 

9. Cloud Deployment Model

There are four main cloud deployment models namely public cloud, private cloud, hybrid cloud, and community cloud. Each deployment model is defined as per the location of the infrastructure. The public cloud allows systems and services to be easily accessible to the general public. Public cloud could also be less reliable since it is open to everyone e.g. Email. A private cloud allows systems and services to be accessible inside an organization with no access to outsiders. It offers better security due to its access restrictions. Hybrid cloud is a mixture of private and public clouds with the critical activities being performed using private cloud and non-critical activities being performed using the public cloud. Community cloud allows system and services to be accessible by a group of an organization.

10. Cloud Security

Since the number of companies and organizations using cloud computing is increasing at a rapid rate, the security of the cloud is a major concern. Cloud computing security detects and addresses every physical and logical security issue that comes across all the varied service models of code, platform, and infrastructure. It collectively addresses these services, however, these services are delivered in units, that is, the public, private, or hybrid delivery model. Security in the cloud protects the data from any leakage or outflow, theft, calamity, and removal. With the help of tokenization, Virtual Private Networks, and firewalls data can be secured. 

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Survey on serverless computing

  • Hassan B. Hassan 1 ,
  • Saman A. Barakat 2 &
  • Qusay I. Sarhan 2  

Journal of Cloud Computing volume  10 , Article number:  39 ( 2021 ) Cite this article

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Serverless computing has gained importance over the last decade as an exciting new field, owing to its large influence in reducing costs, decreasing latency, improving scalability, and eliminating server-side management, to name a few. However, to date there is a lack of in-depth survey that would help developers and researchers better understand the significance of serverless computing in different contexts. Thus, it is essential to present research evidence that has been published in this area. In this systematic survey, 275 research papers that examined serverless computing from well-known literature databases were extensively reviewed to extract useful data. Then, the obtained data were analyzed to answer several research questions regarding state-of-the-art contributions of serverless computing, its concepts, its platforms, its usage, etc. We moreover discuss the challenges that serverless computing faces nowadays and how future research could enable its implementation and usage.

Introduction

Cloud computing emerged after the appearance of virtualization in software and hardware infrastructures; hence cloud providers increasingly adopted it to offer their services to customers [ 1 , 2 ]. Customers can access these cloud services via the Internet. Software developers have been using cloud technologies in their software solutions owing to their benefits including scalability, availability, and flexibility [ 3 ].

In general, cloud computing is divided into three main categories based on the provision of services, which are software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS). In the SaaS category, cloud providers offer different types of software as services to the users. For example, Google provides many applications as a service (e.g., Gmail, Google docs, Google sheets, and Google forms). In this type of cloud, the user is not responsible for the services development, deployment, and management. The user here only uses them without worrying about their settings, configurations, etc. Meanwhile, in the PaaS, cloud companies provide services such as network access, storage, servers, and operating systems to be purchased by developers. The developers access these services to deploy, run, and manage their applications. In this kind of cloud, the developer is responsible for the deployment and management (settings and configurations) of their software to ensure that the application is running, while they do not control the services. Finally, in the IaaS category, the cloud consumers control and manage services such as network access, servers, operating systems, and storage.

Managing cloud services is not an easy task at all. The authors in [ 4 ] have addressed several challenges while managing a cloud environment by a user such as availability, load balancing, auto-scaling, security, monitoring, etc. For example, the cloud user has to ensure the availability of the services in which if a single machine failure occurs, it does not affect the whole services. Also, he/she has to consider distributing copies of the services geographically to protect them when disasters happen. Another challenge is load balancing. In this case, the cloud user has to ensure that requests to the services are balanced to utilize all resources efficiently.

These challenges have led to introduce another cloud computing model, which is called serverless cloud computing [ 4 ]. Serverless cloud computing offers backend as a service (BaaS) and function as a service (FaaS), as shown in Fig.  1 . The BaaS includes services like storage, messaging, user management, etc. While, the FaaS enables developers to deploy and execute their code on computing platforms. The FaaS relies on the services provided by the BaaS such as a database, messaging, user authentications, etc. The FaaS is considered as the most dominant model of serverless, and it is also known as “event-driven functions” [ 5 , 6 ].

figure 1

Serverless architecture

Serverless cloud model was for the first time introduced by Amazon Lambda in 2014, after which cloud companies like Google and Microsoft adopted it in 2016. Serverless cloud computing adds an additional abstraction layer to the existing cloud computing paradigms, while it abstracts away the server-side management from the developers [ 7 ]. Serverless model lets the developers focus on the application logic rather than the server-side management and configurations. For example, the developers deploy their applications to the serverless cloud as functions see Fig.  1 . Then, the cloud provider takes responsibility for managing, scaling, and providing different resources to ensure the smooth running of these functions [ 8 , 9 ].

However, FaaS and the term “serverless” could be used interchangeably, as the FaaS platform automatically configures and maintains the execution context of functions and connects them to cloud services without requiring server provision by developers [ 10 , 11 ]. We refer to the FaaS when we use the term serverless computing.

Serverless cloud computing has many good characteristics [ 12 , 13 ], one of which is scalability. Scaling could be vertical or horizontal; vertical scaling adds or removes cores from the running container, while horizontal scaling creates new containers or eliminates running ones without affecting the current resource allocations [ 14 ]. In serverless computing, the applications automatically scale up and down on demand, and the developer does not have to concern themselves about the scaling issues. For example, when an application runs on a serverless cloud, it will scale up automatically when the application requests increase. Another characteristic of serverless computing is the payment per resource usage. This paradigm of cloud computing charges developers based on the actual resource usage. For example, deploying an application will not cost the developer in the case where the application is idle, and the serverless provider will only charge whenever the application has started using resources.

However, any new technology will face numerous technical and operational issues and obstacles at the beginning. Since the recent introduction of serverless cloud computing, several drawbacks have been identified [ 7 ]. Serverless cloud computing lacks tools that help managing and monitoring serverless applications. Moreover, it might comprise security concerns. Further, the serverless providers have a vendor lock-in problem. Nevertheless, serverless cloud computing has gained positive attention in the industry, despite that it has not been studied extensively in academic research [ 7 ].

Therefore, the aim of this research is to answer some crucial research questions related to serverless cloud computing and thereby help researchers as well as developers to better understand serverless cloud computing and contribute to its development.

The rest of this paper is structured as follows: “ Related works ” section presents the related works for this study. “ Research methodology ” section describes in detail the research methodology used to conduct this survey study. “ Results ” section presents the results and outcomes of the study. “ Threats to validity ” section presents the threats to validity of this study. Finally, the conclusions of the study are provided in “ Conclusions ” section.

Related works

The most relevant studies published on the topic are briefly presented here. The authors in [ 15 ] and [ 16 ] discussed some important background to the origin and evolution of serverless computing and the long road that serverless computing has taken over the years. The authors in [ 9 ] thoroughly discussed the true meaning of serverless architectures and how they are changing the way in which applications are built, deployed, and distributed.

Numerous studies focused on technical interpretations of serverless computing, while other more recent research suggested various benefits that it brings to developers. Nowadays, this type of computing is being used in several ways. In an empirical study, the authors in [ 17 ] aimed to investigate the development practices of serverless computing in the industry. They concluded that for developers, it remains a barrier to adopt the right mindset to best utilize the tools inherent to serverless architecture. More documentation and easier access to such resources would help developers to embrace the possibilities that serverless computing has to offer.

The concept of serverless computing within the scope of the IT industry has the great potential of progressively increasing its capabilities to involve a wider set of domains. Thus, the implementation of serverless computing is not restricted only to the enhancement of infrastructure, and it can be employed for many different purposes, e.g., serverless messaging, neural network training [ 18 ], video processing [ 19 ], and big data [ 20 ]. Undeniably, their contributions are valuable to the general public and researchers in the field, as it is of primarily importance to comprehend how this technology works.

However, it is presently crucial to provide more than only theories and concepts: it is time to weigh the benefits and drawbacks of serverless computing and to analyze how far the field has progressed, to assess what remains to be done and improved. As an example, the authors in [ 21 ] discussed some possible new abstraction levels to reduce processing limitations. The authors in [ 22 ] discussed the results from an open-source framework to achieve on-premises serverless computing that can process big workloads with a scalable and sensible usage of resources. We can infer from these related publications that researchers everywhere are working to determine how to best exploit the potential that serverless computing frameworks could introduce to software development.

In [ 23 ], the authors described how serverless computing is becoming the next step in the evolution of cloud computing and its platforms. In our paper, we focus on the ongoing challenges, benefits, and drawbacks of using it.

The authors in [ 24 ] have conducted a systematic exploration of serverless computing-related research papers. As they mentioned, their work is not a survey, but it is a supporting source for future research papers. They proposed an open dataset for serverless computing papers. The dataset includes 60 papers for the period (2016-July 2018). Also, they have analyzed the dataset according to bibliometric, content, technology, and produced statistics about each section. In contrast, our paper aims to conduct a systematic survey. In this survey, we try to find answers to several critical questions related to serverless computing. In addition to that, our study covered the duration (2016–2020) and thus 275 papers have been considered.

The authors in [ 25 ] mainly focused on scheduling tasks in the cloud. They described the various techniques in scheduling workflows to reduce the execution time, cost, or both. Moreover, they proposed a hybrid method by both FaaS and IaaS. The small tasks could be executed remotely using the FaaS, which reduces the execution cost; hence, the number of virtual machines will be decreased as well. Therefore, the whole focus would be on the long-running tasks on IaaS.

The authors in [ 26 ] covered only 24 research papers during 2017–2019. In their paper, they considered both the white and grey literatures. Besides, they identified 32 characteristics of serverless and the possible issues related to them, only eight of them were stated by both white and grey literatures while the remaining are from grey literature only. All the characteristics are explained and presented briefly. In our paper, 275 research papers from 2016–2020 have been covered and more research questions have been answered. Besides, a well-defined systematic literature study process has been employed. Thus, the grey literature has been excluded in our paper and, our results are reproducible compared to their results.

The authors in [ 27 ] mainly concentrated on difficulties and gaps in data-centric and distributed computing using FaaS. Additionally, they evaluated the severity of these challenges via taking three case studies from big data and distributed computing settings: model training, low-latency prediction serving using the batch and, distributed computing. While our paper is a broad and comprehensive study on FaaS, 275 research papers are taken from the white literature during 2016–2020.

The paper [ 28 ] presented only four use cases of FaaS: event-triggered computing, video broadcasting, Internet of Things (IoT) data processing, and shared delivery system. Additionally, the paper only compared three platforms namely, Amazon web services (AWS) Lambda, Google Cloud Function, and Microsoft Azure Function. On the other hand, our paper presents a comprehensive study about FaaS. We identified in detail eight use cases: chatbot, information retrieval, file processing, smart grid, security, networks and, mobile and IoT. Moreover, our paper compared ten FaaS platforms namely, AWS Lambda, Apache OpenWhisk, Microsoft Azure functions, Google Cloud functions, OpenLambda, IBM Cloud functions, OpenFaaS, Knative, FunctionStage, Huawei Cloud, and Nuclio.

The authors in [ 29 ] covered only 15 papers during 2016–2018. They took both the white and grey literatures into account. On the other hand, our paper includes 275 research papers published in the period 2016–2020; they are taken from the white literature only. Moreover, our paper has formulated and answered eight clear and well-defined research questions.

The authors in [ 30 ] focused on the FaaS performance evaluation and their publication trends during 2016–2019. They identified the most commonly evaluated FaaS platforms. Additionally, they evaluated the performance features for benchmark types, micro-benchmarks, and common features across FaaS platforms. Moreover, they presented the most common platform configurations in FaaS, namely language runtimes, function triggers, and external services. This paper presents a survey of the most important and state of the art aspects of FaaS. Besides, comprehensive theoretical aspects of FaaS are covered taking from the white literature during 2016–2020.

The authors in [ 11 ] have conducted a systematic mapping study on serverless cloud computing. The main aim of their study is to concentrate on FaaS engineering. They raised two main concerns: (a) identifying publication research that considers developing or modifying serverless platforms and tools. (b) identifying the challenges and drivers related to these publications. On the other hand, our study extends the challenges and issues related to serverless computing. Moreover, we provide more details about serverless computing platforms and the use of these platforms in the literature. Also, it provides a detailed comparison among the most widely used serverless platforms. Besides, it addresses more aspects of serverless cloud computing such as application areas of serverless computing, future directions of serverless computing, etc.

The authors in [ 7 ] provided useful observations about serverless computing, its architecture, and use cases. Also, they discussed the challenges and benefits of moving forward from monolithic applications and the differences between traditional cloud services and serverless computing. Our work has extended the details of their work regarding the benefits and drawbacks of using serverless computing. It has also included more use cases and workloads to deepen the findings of previous studies.

The authors in [ 4 ] presented a technical report on serverless computing. They covered the serverless emergence with its limitations, including limited storage for fine-grained tasks, lack of coordination among functions, inadequate performance for standard communication patterns, and functions’ performance. Also, they compared AWS serverful with AWS serverless. Moreover, they also explained the challenges of architecture, networking, security, and abstractions of serverless computing. They identified five application areas including, video encoding in real-time, MapReduce, linear algebra, machine learning training, and databases. While our paper has covered 275 research papers from 2016–2020 forming a well-defined systematic literature study. We also identified 21 serverless challenges and issues. Besides, we compared serverless with the traditional cloud computing paradigm. We identified more application areas including, chatbot, information retrieval, file processing, smart grid, security, networks, IoT, and edge computing.

The authors in [ 31 ] presented a white paper based on published research papers during 2015–2017. They outlined the serverless definition alongside its advantages and disadvantages. Also, they classified serverless use-cases into six categories, namely, backends, web applications, chatbots, big data, IT automation, and Amazon Alexa. Moreover, they addressed a few research questions including, the need for the stateless feature in serverless, whether serverless could execute long-running tasks, programming models, serverless standards, and the importance of serverless for scientific research. While our paper is a comprehensive study on FaaS; we covered 275 research papers which are taken from the grey literature during 2016–2020. In our paper, eight application areas have been identified as mentioned earlier. We have identified and answered ten research questions that cover many aspects of the topic in detail compared to the aforementioned study.

We are in fact addressing with this paper ten important research questions about the topic, potentially making it a more complete guide to the development and use of serverless computing. Our work contributes to the analysis of the serverless paradigm in the context of similar applications and how could they better fit specific computing needs. Moreover, information about the current state of serverless platforms, tools, and frameworks has been updated for this survey. This due to the importance of the topic and its potential to change how both the industry and academia have managed the deployment of cloud applications until now. Updated information about the area could benefit future studies focused on the serverless computing paradigm as they make researchers aware of the latest resources and opportunities in the area.

Research methodology

Research questions.

In this study, a number of research questions (RQs) have been identified and answered. Each RQ addresses a particular aspect of serverless computing as follows.

RQ1. What is the number and distribution of studies published on serverless computing in the period (2016–2020)?

RQ2. Which researchers, organizations, and countries are active in serverless computing research?

RQ3. What are the differences between serverless computing and traditional cloud computing?

RQ4. What are the benefits of using serverless computing?

RQ5. What are the most used software platforms that enable serverless computing in the literature?

RQ6. What are the application areas of serverless computing in the literature?

RQ7. What are the challenges and issues of using serverless computing?

RQ8. What tools are available for serverless computing? (serverless tools)

RQ9. What are the available research approaches to analyze the migration of monolithic applications to serverless computing?

RQ10. What are the potential future directions of research on serverless computing?

Search strategy

Literature sources.

In this study, five standard online databases have been selected as sources that index the literature of software engineering and computer science. These sources are presented in Table  1 .

Search string

To find the publications relevant to this study, the following extensive search string has been applied on the database sources of literature:

(serverless OR FaaS OR “function as a service” OR “function-as-a-service”) AND (computing OR paradigm OR architecture OR model OR application OR function OR service OR platform OR programming)

To obtain the best publication list, a generic search string is created. It contains serverless cloud computing-related keywords. The string with duration (2016 - 2020) have been applied to all libraries. Because the Springer Link library covers many fields, the result of search was greater than other libraries. This because the keyword FaaS is used in many research areas for different purposes. For instance, fish as a service (FaaS) and FPGA as a fervice (FaaS). Therefore, we used Computer Science subject filter with Springer Link, ScienceDirect, and Scopus to reduce the number of incorrect papers. The results of the initial search are shown in Fig.  2 . Additionally, some inaccurate results have been obtained due to the partial similarity to FaaS, such as the federal aviation administration (FAA). The results of the initial search were 5,021 papers in total.

figure 2

Results of papers selection process

After obtaining the initial list of publications, some filters have been applied to reduce the number of incorrect results based on their relation to the serverless computing and FaaS topics. Most of the papers have been analyzed based on the title and abstract. However, when we were unable to make a decision based on the title and the abstract, we read the content of the paper to ascertain whether to include or exclude. As a result, the list of papers which are related to serverless computing has been decreased to 549 papers.

After filtering the papers based on the title and abstract, we merged all the papers that were relevant to serverless cloud computing, which was 549 papers into a single dataset. Then we removed the duplicated papers based on the combination of a title, author names, publication year, and venue. Thus, the number of publications has been reduced to 489 papers.

Then, the publications have been selected based on the content of the paper and based on a set of inclusion/exclusion criteria (see the following section) that have been selected carefully. Eventually, we could obtain 254 papers that are related to serverless cloud computing. In the next step, we applied backward snowballing to increase the set of relevant papers to serverless cloud computing. In this phase, we could add 21 more papers to our list of papers. As a result, the total numbers of relevant papers become 275 papers. The list of these papers and its meta-data have been published in Zenodo website as a dataset [ 32 ].

Paper inclusion/exclusion criteria

To decide whether a publication is relevant to the scope of this research, a set of inclusion and exclusion criteria have been established and employed as follows:

Inclusion criteria:

Publications in the field of software engineering and computer science.

Publications published online from 2016 – 2020.

Publications related directly to serverless computing.

Exclusion criteria:

Publications not published in English.

Publications without accessible full text.

Publications not formally peer reviewed (e.g., gray literature).

Publications not published electronically.

Publications that are duplicates of other previous publications.

The selected publications were carefully read to answer the raised RQs. Here, a short title is used to represent each RQ. The following subsections present and discuss the results based on each RQ.

Distribution of publications (RQ1)

Publication frequency.

All the selected papers of this study were analyzed to determine their frequency and evolution. Figure  3 shows the results of this analysis. The results show that the average number of publications per year is approximately 55 papers.

figure 3

Published papers per year

Serverless computing has trended a significant engagement over the past two years. This boost has been caused by industry, academia, and developers for several reasons. The first important reason is the attractive engagement opportunities that serverless offers cloud providers. Serverless nature equipped cloud providers with more convenient and efficient methods to manage and utilize idle computing resources. Another reason is that the billing is only on the basis of function execution time and resource allocation. Also, the developers are not required to be aware of the underlying infrastructure and workflows. Hence, this attracts cloud providers and businesses to migrate and support serverless alongside many directions. At the same time, researchers are paying more attention to serverless as it is becoming the future paradigm for cloud computing. Moreover, current challenges and limits in serverless computing draw attention to more academics to address the issues and enhance the currently available features. For the aforementioned reasons, developers and customers are well encouraged and satisfied to select serverless computing for developing applications and services.

Publication venue

The distribution of the selected papers in various publication venues is shown in Fig.  4 . The percentages of publications in conference papers, workshop papers, symposium papers, and journal papers are approximately 62%, 11%, 14%, and 13%, respectively. However, almost 13% of the studies have been published in journals, which indicates the immaturity of research in serverless computing [ 33 , 34 ]. It is worth mentioning that some conference papers were published as book chapters. Thus, the original venues, which are conferences, of such papers were considered.

figure 4

Published papers ratio per each venue

Following the interpretation of publications, the most productive and primary journals, symposiums, conferences, and workshops venues related to serverless computing can be clarified. Due to their long names, abbreviations are used in this paper. The active journals are shown in Fig.  5 and their full names can be found in Table  2 . It can be observed from the figure that the top and vital three journals are “FGCS”, “IoT”, and “JSS”. Also, it can be noticed that the top three journals contain almost 34% of the published journal papers, while the others own approximately 66%.

figure 5

Published papers vs. journal name

The active conferences are shown in Fig.  6 and their full names are presented in Table  3 . The “WOSC”, “Cloud”, “UCC”, “SoCC”, and “Middleware” are considered the most active conferences that hold approximately 28% of the published conference papers. By including other conferences with three published papers or more, then approximately 23% of the conference papers are published in annual conferences. The majority (almost 49%) of the conference papers were published at individual conferences, which are denoted as “Others” in Fig.  6 .

figure 6

Published papers vs. conference name

Active researchers (RQ2)

Serverless computing is a vital research area through the contribution of several scholars. Yet, the researchers are counted active if they contributed to more than two research studies, as presented in Fig.  7 . The figure shows that the top six active researchers are “Pedro Garcáa López”, “Erwin Van Eyk”, “Alexandru Iosup”, “Marc Sánchez-Artigas”, “Sebastian Werner”, and “Wes Lloyd”. Table  4 presents the active nations, research institutions, researchers, references to the published papers, and the total number of publications.

figure 7

Active researchers based on the published papers

The active nations in the number of papers are obtained from the information presented in Table  4 by extracting the institutional affiliation of the authors and co-authors. An overview of the most active nations and the total number of publications is shown in Fig.  8 . It is observable that the United States and Germany are the largest contributors to papers published on serverless computing with 104 and 39 published papers, respectively.

figure 8

Active countries

Serverless computing vs. traditional cloud

computing (RQ3) There are several differences between serverless and traditional cloud computing. In the traditional cloud architecture, the server acts as a monolithic system containing all business logic. Meanwhile, the serverless architecture is modeled into smaller, event-driven, and stateless ‘triggers’ (events) and ‘actions’ (functions) [ 175 ]. Each component handles different pieces of data and runs independently [ 176 ]. Spreading business logic into smaller functions increase the development efficiency [ 77 , 177 ] and also decreases the chance of a single point of failure [ 77 ]. On the other hand, the component dependency within monolithic applications affects the availability of other services adversely.

In a serverless architecture, the developers are unable to take control of listening to the TCP socket, managing load balancers, maintenance or configuration of the server, as well as provisioning and resource allocation. Therefore, there is no need for system administrators; the developers only focus on handling client requests and paying attention to deliver valuable services [ 8 ].

Serverless computing also differs from monolithic computing as the functions have shorter life cycles.

The traditional monitoring and debugging tools that are used in monolithic applications are not included in the serverless architecture; the developers are compelled to use built-in tools for debugging and monitoring. The computing power is no longer a concern for the developers in the serverless paradigm, as it could scale horizontally almost indefinitely [ 178 , 179 ]. Meanwhile, in the client-server architecture, it usually requires dedicating two server instances; the primary instance and a second in case if the former fails. This leads to higher costs in the monolith paradigm. Serverless architecture could be more economical for unsteady load conditions while the server-based is more suitable for steady loads [ 152 ]. As serverless applications scale up and down according to the requests, thus, unlike the traditional systems, it is unnecessary to keep the sessions in the memory [ 8 ]. Hence, it is difficult to keep track across requests.

FaaS boosts the security level as cloud providers continuously update their infrastructure with the latest security patches; this also removes the security burden on developers [ 17 ]. Directly accessing the backend resources in the traditional model is considered a critical security issue. Thus, any requests from the clients and internal functions in the serverless environment must go through a distributed request-level authorization mechanism that strengthens the security level [ 8 ]. Additionally, denial of service (DoS) attacks are controlled, as it is more difficult to attack distributed servers than a single server [ 175 ]. However, some security concerns remain due to the third-party API usage [ 9 ]. Besides, there is a lack of tools to identify vulnerabilities and access control risks. Table  5 summarizes the aforementioned differences.

Benefits of serverless computing (RQ4)

Serverless computing offers numerous benefits to its users, and Table  6 presents papers that states these benefits. This section summarizes those benefits as follows:

Cost effective

Serverless applications are abstracted from server infrastructure, which results in cost-based services depending on usage [ 180 ]. For example, applications run whenever a user makes a request to a service within the application. The cloud vendors charge only for the used space, and there is no cost while their applications are in an idle state.

Scalability

Serverless reasonably solved the resource allocation problem [ 191 ]. Therefore, developers do not have to concern themselves with the application scalability, because the application will scale automatically whenever user application requests are increased. If there are numerous requests to a function within the application, the serverless providers will start servers to handle these requests.

Server-side management

In serverless computing, developers do not need to concern themselves with the server-side and its management. Serverless cloud providers take care of managing and maintaining the hardware and software required to deploy applications. In addition to that, they handle all administration operations to let developers focus on different kinds of resources such as central processing unit (CPU), memory, and storage.

Easy to deploy

Serverless applications are easy to deploy. For example, to deploy an application, developers only need to upload some functions and release a new product. The serveless will take care of deployment management and infrastructure related concerns such as server provisioning and scaling.

Decrease latency

Serverless applications are not hosted on a specific server; the code can run from any server in any location. Therefore, cloud vendors can run the application on servers near the end user’ location. This reduces latency, because end user requests do not have to travel across the Internet to access the original server.

Serverless platforms in the literature (RQ5)

The software platforms are generally implemented to deal with resources from several clouds and ensure proper running of client applications. The heterogeneous nature of the cloud providers’ infrastructure (hardware and operating systems) led to the necessity to direct the developers’ focus to the functional part, rather than the underlying infrastructure [ 199 ].

With the emergence of the first serverless platform, AWS Lambda by Amazon in 2014 [ 8 ], cloud computing has evolved to a new generation referred to as serverless computing. However, serverless was not a brand-new paradigm; it emerged after the advancements in adopting virtual machines and container technologies [ 120 ]. By 2016, other competitors, namely Google, Microsoft, and IBM followed the trend. Several commercial and open-source platforms offer serverless computing. The well-known commercial systems are AWS Lambda, Google Cloud Functions, and Azure functions. Alternately, there are several open source platforms available including IBM Cloud Functions, and Apache OpenWhisk.

There are several criteria to help developers in selecting a serverless platform: cost, performance, supported programming languages and model, deployment easiness, easiness in composing functions from different providers, security, and monitoring and debugging tools [ 184 ].

Table  7 presents the serverless platforms used in the considered papers of this study. It can be noted that “AWS Lambda”, “Apache OpenWhisk”, and “Azure Functions” are the most used platforms with 78, 23, and 11 published papers, respectively. However, it is worth mentioning that each platform has its own set of features and differs from others.

The application areas of serverless computing in the literature (RQ6)

Serverless computing can be utilized in a number of application areas as follows:

A chatbot application is developed using serverless computing, which enables interaction with users via voice or text commands. Within this application, six functionalities have been implemented, namely the Date, News, Jokes, Weather, Music Tutor, and Alarm Service. For example, a user can ask for the current date using a voice or text command. The request is routed to a required serverless action on OpenWhisk for further processing. The Date action is activated via the issued command and retrieves the current date to the user in the form of text or voice [ 44 ].

Another example is the ticketing chatbot service developed using serverless computing and natural language processing (NLP). The architecture of the system consists of three parts: (1) the node.js Webhook, which works based on hypertext transfer protocol (HTTP) POST or GET requests (2) Wit.AI, which is a NLP service (3) Ticket.com, which is a ticketing order API. For example, when a user books a flight ticket; a specific function on Webhook will be activated, which routes the user query to the Wit.AI service. Wit.AI will process the query and extract useful parameters from the request such as destination, date, and time, then send it back to Webhook. After receiving the processed query from Wit.Ai; another action will be triggered and pass the processed query to Tickt.com API to retrieve flight information such as the flight number, airline name, departure time, and ticket price from several airline companies. Finally, Webhook will provide flight information to the user [ 44 , 179 , 248 ].

Information retrieval

A search engine web-based application is developed based on serverless architecture. Search engine functionalities are implemented as Amazon lambda functions. The search engine executes all the details of retrieval processing after receiving the user query (e.g., tokenization, stop-word removal, term weighting, similarity calculation, and ranking). Then, it sends back the results to the user as documents stored in the DynamoDB database to be accessed using the web application interface [ 173 ].

File processing

Serverless computing can been utilized in file processing applications [ 119 , 249 ]. For instance, in [ 119 ] a model for highly parallel file processing applications based on serverless architecture is proposed. This model provides users with different ways to process their files.

The first method is by using the API gateway. In this method, users submit files using the HTTP request employing the API gateway to a lambda function to process the file (e.g., medical images and video files).

The second method is by uploading/reading files to the Amazon simple storage service (Amazon S3) bucket. This method provides the user with three different ways to execute a lambda function using S3 buckets: (a) by uploading a file to S3 buckets. When the file is uploaded, S3 creates an event to invoke a lambda function; (b) by copying a file from another bucket to the bucket linked with the lambda function. This will cause the trigger of an event from S3 to invoke a lambda function as in the previous manner; (c) by specifying a bucket where the files to be processed are stored. Then, for each file found, the lambda function is invoked in parallel using an S3 bucket.

The third method is by specifying the output file. By this method, the user can set a chain of lambda functions to be invoked by S3 buckets. In this case, the user defines the input/output buckets for each of the lambda functions. Thus, the output bucket can be used as an input to another lambda function [ 119 ]. Here, serverless functions can handle different types of data (stored in files) such as sensory, textual, and biological data [ 200 ]. Also, many preprocessing operations using NLP may be applied to data files before processing, such as stemming and noise removal [ 78 ].

A MATLAB simulation scenario is created to illustrate the use of the smart grid with serverless cloud computing to control and manage electrical loads (devices). In this scenario, the Simulink tool is employed for simulation. A MATLAB program is developed to indicate the start and end of the simulated grid model via a batch file. The batch file is used to upload grid model data generated by the program to Amazon S3. Afterwards, a lambda function in the serverless side will be activated to process the uploaded data, and subsequently the result will be sent back to the batch file as a response. In return, the program will read continuously the response from the batch file and interpret its content to manage the electrical switch (loads) [ 201 ].

Also, An electrical overload warning system is implemented in the smart grid, based on serverless architecture. The system uses some Amazon web services, including S3, lambda functions, simple notification service (SNS), and CloudWatch. S3 is used as a storage service in the system. Lambda functions constitute a computing service that executes the code of the application. CloudWatch is a monitoring tool that monitors AWS resources and applications. The SNS is a notification service that sends and receives notifications.

The main sections of this warning system consist of data collection, data acquisition, data analysis, data mining, conclusion verification, and conclusion publishing. In this architecture, the AWS Lambda is used in data analysis and data mining. AWS CloudWatch is used for data conclusion verification. The SNS is used to generate alarms. For instance, the data is uploaded to S3, and subsequently, a lambda function is activated for data analysis and data mining. After the lambda function execution, its log data is stored in CloudWatch logs. CloudWatch is used for conclusion verification. CloudWatch defines an alarm size to a specific value, upon which it compares the value of log data with a predefined alarm size to check the current state. Then, the CloudWatch uses SNS for publishing conclusions. If the receiving data is greater than the alarm size, an alarm signal will be triggered and send an email via SNS [ 5 ].

An automated threat detection system is introduced using serverless cloud computing and Kubernetes. Kubernetes is an open source system to automatically deploy and manage application containers [ 243 , 250 ]. The system deals with threats (e.g., software vulnerabilities and insecure configurations) automatically based on user-defined policies. The system includes a vulnerability scanner (VS), which is a thread detection component. Whenever users deploy new application containers, the containers are registered with the VS, and a scanner agent is installed. When a thread is detected by the scanner, a notification is sent to the OpenWhisk component, which activates a serverless function that takes actions to reduce the threat. OpenWhisk will invoke a Kubernetes API extension and let the security enforcement operator (SEO) handle the operation [ 35 ].

Serverless cloud computing has been employed in different networking domains[ 175 , 188 , 251 , 252 ]. In [ 188 ], a variety of networking fields including software-defined networking (SDN) which can utilize advantages of serverless computing architecture have been discussed. The SDN is a network architecture approach that enables the network to be manageable and adaptive. This architecture separates the network control plane from the forwarding functions (the data plane). This decoupling enables network switches to become a simple forwarding device, and the network control is implemented as a network application that executed on a logically centralized controller. Serverless computing can be used in the SDN controllers. These controllers can be implemented as independent functions deployed on serverless platforms. For example, when a packet arrives to the SDN forwarding device, the device will parse the packet header and forward it to the SDN controller. The functions within the SDN controller will be activated then it will determine what action to be taken with the packet. After that, it will send the information to the forwarding device. The action might be modifying the header, dropping the packet, etc.

Serverless computing has been utilized in many IoT applications, as shown in Table  8 . For example, a camera can be installed to monitor a house, after which processing images captured by the camera can be performed by some serverless functions provided by the OpenWhisk platform. When a camera detects an interesting object such as a car or a human, the camera sends its pictures to the serverless platform for further processing. To extract features, a serverless function is called to perform feature extraction and then reports its status to the users [ 232 ].

Edge computing

Serverless cloud computing and edge computing have been used to build different kinds of applications, as presented in Table  8 . For instance, the authors of [ 217 ] have implemented an autonomous mobile robot (AMR) system based on serverless computing and edge computing. The system consists of three main components: an AMR with NVIDIA Jetson TX2 module for edge computing, a serverless architecture based on AWS, and a cross-platform mobile application developed using React Native. The main idea of the system is to deliver a package to a user. For example, the user will interact with the mobile application to send a package. Once the delivery request has been received from the user, the AWS IoT can activate related lambda functions, such as position coordinate. Then, the AMR would start its mission, sending the package to the receiver’s location. Also, facial images were regularly retrieved by AWS lambda to identify the receiver’s face. Finally, the task is completed when the receiver’s identification is confirmed [ 217 ].

Serverless computing challenges and issues (RQ7)

Studying the literature reveals a number of challenges and issues posed by employing serverless computing. These challenges cover the functional and non-functional aspects of serverless computing as follows:

Cost and pricing model

Cost is a fundamental challenge; therefore, serverless computing providers should reduce the usage of resources to the minimum, while functioning in both execution and idle states. Further, the pricing model is another challenge in serverless computing compared to other cloud computing approaches. For example, the CPU bound is cheap, whereas the input/output (I/O) bound functions may be more expensive from dedicated servers. Table  9 presents papers that investigate issues on cost and pricing models in serverless cloud computing.

Serverless computing can scale to zero while there is no request for functions and services. Scaling to zero leads to a problem called cold start. A cold start occurs when serverless functions remain idle for some time, and the next time these functions are invoked, a longer start time is required. Methods and techniques to reduce the cold start problem are crucial as a result, many papers have been studied this problem, as shown in Table  9 .

Resource limits

In serverless computing, resources are required to ensure that the platform can deal with load increasing. This includes CPU usage, memory, execution time, and bandwidth [ 94 , 202 , 210 , 235 , 280 ].

Security is the most challenging issue in serverless cloud computing. One of the security issues is isolation, because functions are running on a shared platform by many users. Therefore, strong isolation is required. Another security issue is trust when it comes to process-sensitive data. The serverless applications work with many system components, which must function correctly to maintain security properties. Table  9 presents several papers associated with serverless security.

Serverless computing must ensure function scalability and elasticity. For example, when many requests are issued to a serverless application, these requests should all be served and the used serverless cloud provider should provide the required resources to process all these requests and should scale up with the number of requests [ 210 , 280 , 281 ].

Long-running

Serverless computing runs function in a limited and short execution time, while there are some tasks might require long execution time. This does not support long execution running, since these functions are stateless, which means that if the function is paused it cannot be resumed again [ 11 , 202 , 234 , 280 ].

Programming & debugging

There is currently a lack of debugging tools. Further, monitoring tools are required, since developers need to monitor the application and observe how functions are working. More advanced integrated development environments (IDEs) are needed, so developers can perform refactoring functions, such as merging or splitting functions, and reverting functions to the previous version. Moreover, logs from serverless function invocations need to be sent to the developer and provide detailed stack traces. When an error occurs, a good method is required to report details on the occurrence to the developer. The equivalent of a stack trace for serverless computing is currently not available. Table  9 shows many papers that consider programming and debugging challenges and issues.

Vendor lock-in

The FaaS paradigm separates the code from the data, which leads the functions to depend strongly on the could provider’s ecosystem for storing, obtaining, and transferring data [ 282 ]. This issue makes the customers dependent on the serverless provider for products and services, and the customers cannot easily use different vendors in the future without substantial cost. Thus, customers have to wait on the serverless provider for additional services [ 9 , 130 , 202 ].

Performance

Serverless computing has many performance challenges and issues such as scheduling and service calling overhead. For instance, scheduling means when a serverless function is activated in response to an event this function should be mapped to a specific resource (e.g., container or VM) to be run. The resource can have a significant effect on performance based on available resources, location of input data and code, load balancing, etc. Table  9 shows papers related to serverless performance.

Fault tolerance

It refers to a system that continues working and provides its services despite the failure in some components. It mostly occurs when some containers fail. To overcome this challenge, a basic retry mechanism is used [ 11 , 210 , 235 ].

Function composition

Serverless cloud vendors provide users the ability to deploy small stateless functions to the cloud to handle a specific task. However, some complex tasks require multiple functions to work with each other collaboratively to be performed. Therefore, more research needs to be done on how function composition can be used effectively and efficiently in serverless cloud computing [ 11 , 38 , 235 ].

Resource sharing

Functions in serverless cloud computing share resources to achieve inexpensive cloud computing. Sharing resources among functions and other serverless components is a challenging task. Therefore, good techniques are required to be investigated to achieve this goal [ 98 , 210 , 283 ].

A serverless application consists of many small functions. These functions work together to accomplish the application’s functionality. Therefore, integration testing for these functions is a crucial issue to make sure that the application works properly [ 9 , 84 , 284 ].

Naming and addressing system

Users deploy functions to serverless cloud computing to solve problems. These functions cannot listen to network communications. The existing serverless cloud computing frameworks do not support this feature. Instead, they use third party services such as Amazon S3 to communicate with other functions. Therefore, finding the internet protocol (IP) address of a function by other functions and services is a challenging issue in serverless cloud computing [ 98 ].

Legacy systems

Legacy systems refer to old technologies, techniques, hardware, and software systems that are still in use. It should be possible to reach these systems from serverless cloud computing. Also, these systems might be required to be transferred to cloud computing. Therefore, more work needs to be done on the migration process and how the functions can be extracted from legacy systems to be deployed as serverless cloud functions [ 84 , 119 , 120 , 210 , 280 ].

Managing hybrid cloud

In a hybrid cloud, a developer may deploy an application to different clouds (hybrid cloud). For example, if some functions of an application are on a specific serverless cloud vendor and others are hosted on other public clouds; then, managing these functions and their interactions is a challenging issue [ 84 , 210 , 280 ].

Lack of quality of service (QoS) support

Existing serverless platforms and frameworks do not provide users the control over the QoS of serverless functions [ 235 ]. Cold starts, queuing, and orchestration are the main reasons affecting the QoS in serverless computing [ 8 ].

Architecture complexity

A serverless application may consist of several functions; the number of functions increases the complexity of the architecture. Managing these functions and services related to the application also leads to a complex architecture [ 9 ].

Interactions tracking

Stateful requests are usually used by real-life applications. It means deployed systems keep track of the state of users’ interactions and store them on the server-side for further uses. However, in stateless serverless functions, it is not obvious how these functions will handle and manage the states of each user [ 210 , 280 ].

Concurrency management

Concurrency means a function can handle any number of requests whenever a function is invoked. For example, if a request has been made to a serverless function, the function will process that request. However, if another request has been made to that function and the function is still processing the previous request, then the serverless should provide another instance of that function to serve the new request [ 210 , 280 ].

Support for heterogeneous hardware

Existing serverless platforms may not support some specialized hardware such as graphics processing unit (GPU) and field programmable gate arrays (FPGAs). This is a challenging issue for vendors to provide support for heterogeneous hardware [ 210 , 280 ].

Tools available for serverless computing (RQ8)

Nowadays, various providers strive to facilitate the adjustable use and allocation of machine resources on the cloud [ 9 ]. Likewise, plenty of supportive tools and services are aiding developers to more efficiently manage and deploy applications using serverless computing. Serverless computing is auto-scalable, reliable, and easily accessible [ 203 ]; for these reasons, big cloud providers such as Amazon, Microsoft, Google, IBM have realized the importance of offering frameworks, IDEs, software development kits (SDKs), function development kits (FDK), migrating mechanisms, logs, and monitoring tools to enhance and simplify the development, testing, deployment, and monitoring of serverless applications [ 17 ]. For instance, Amazon offers Cloud9 IDE for local deploying and testing [ 205 ].

Apart from the cloud providers’ specific tools, plenty of third-party tools exist for the developers. With the concept of these tools, developers can build and deploy applications on multi-cloud providers. Developers are also able to control platforms and resources by programming. The advantages of this are linking the applications with auto-scaling controllers and including advanced self-mechanisms into the code to automatically configure, secure, optimize, and recover the cloud applications. The core advantage of this feature is the acceleration in applying changes to the application environment [ 272 ].

There are several tools available to model serverless applications, which are based on deployment models as either imperative or declarative. The imperative model defines the execution steps to obtain a specific deployment task. While the declarative model describes the structure of a desired application deployment. However, to fully benefit from employing a serverless architecture, cloud providers should address issues that have arisen with the use of a serverless paradigm. For instance, debugging tools are unable to track and identify the exact reason behind errors [ 44 ], as most of them are limited to what cloud providers offer [ 179 ]. Although many powerful tools have been mentioned in this study and can be used in serverless computing in real scenarios, there is still a great opportunity to develop further tools and services.

Migration of monolithic applications to serverless computing (RQ9)

The nature of most existing applications is monolithic. Monolithic applications have several drawbacks; they are characterized by continuous growth in complexity and size over time.

The bigger size of the monolithic applications leads to slower startup time. Moreover, novice developers face difficulties in digesting the traditional programming paradigm. Economically, monolithic systems take more effort to be developed and debugged. Furthermore, integrating the latest technological development into monolithic systems is a tough and expensive process. Generally, monolithic applications are designed to be tightly coupled – the entire application will be unable to run or compile if one component is missing or fails [ 128 ]. It is also difficult to scale the application when multiple components have limited resources.

Another drawback is that updating any component will require redeployment of the entire project. The migration process to serverless computing involves transferring the legacy application code to serverless functions. This process could be more efficient and functional in applications with less size [ 76 ].

The key challenging aspect of migration is about extracting the serverless computing from the monolithic systems. There are several approaches to accomplish this task, one of them is Lift and shift [ 205 ]. This technique transfers the whole infrastructure to the cloud, however, this method also brings the already existing problems within the source to the destination. In [ 205 ] the authors proposed toLambda to automatically refactor, test, and deploy the monolithic applications (Java) into microservices (AWS Lambda Node.js). While rebuilding the legacy application from scratch is recommended for applications that no longer depend on the existing cloud services [ 130 ].

However, not all applications are suitable for migration to serverless computing [ 76 , 128 ]; therefore, the first important aspect to be considered before rebuilding the applications is whether it would save money [ 188 ]. For such cases, newly desired features could be implemented and added via serverless computing as an extension to the current systems [ 128 ].

The other approach is to refactor the entire legacy code into FaaS services. During the migration phase, it is crucial to address the coupling of the systems not only in the application logic but also in the databases, as more functions will call the same database. However, migrating the server-side while keeping the user interface could lead to problems. Moreover, the client cannot obtain integrated data by a single request. As the functions are decoupled into smaller entities, the server is unable to aggregate data from different entities. Thus, it is the client’s responsibility to call the necessary entities to achieve this task [ 76 ].

Future directions of research (RQ10)

As the evolution of serverless computing is relatively new, there are several research paths available to be focused on as follows:

Function startup

One of the major research opportunities is overcoming the cold start problem without affecting the primary feature of serverless which is scaling to zero [ 160 , 188 ]. The first call of functions needs initializing the required libraries, which will cause a cold start. To bypass this, the computing resources will be warm for a certain time. Hence, upcoming requests will be handled faster. This could be performed via enhancing scheduling policies and developing more accurate function performance measurements [ 86 ]. Serverless providers follow their approaches to keep the functions in the warm pool. However, most of them are based on the number of requests for a certain time. Thus, if a function is not called frequently, it will suffer again from the cold start.

Very few studies such as [ 272 ] suggested a periodic event scheduler for Kotless (a serverless framework for Kotlin) which will trigger a list of warm functions every few minutes. The authors of the study claimed that this will reduce the cold start without bringing extra costs. While in [ 233 ] argues that pre-warming methods are unnecessarily using resources with idle containers. The researchers are still working to avoid cold start by reducing high delayed function startups via optimizing compute resources [ 11 ].

Recycling and rebalancing minutes and hours of idle runtime is an expensive process for cloud providers. Therefore, reducing the cold start penalties will help cloud providers in the first place and hence customers. The authors in [ 202 ] proposed FaaStest an autonomous approach based on machine learning to capture the function call behavior and then dynamically select the optimal ones. This technique could reduce the cold start by 90%. They proposed a strategy to predict functions invoking time and warming the function using fine-grained regression method [ 285 ]. However, overcoming the issue of function startups is still considered as a research direction to be more investigated.

Keeping a guaranteed QoS level in the software level agreement (SLA) that describes the lower service level offered by the service providers [ 166 ] is a major obstacle for cloud providers to offer optimal performance metrics [ 167 , 207 ]. However, serverless frameworks should consider the objectives of both providers and users [ 242 ]; customers and developers have none or little QoS support over the functions [ 236 ]. In addition, the auto-scaling feature lacks QoS guarantees. This lack of QoS affects the performance of serverless applications. Increasing response time leads to decreasing the QoS level [ 207 ]. It also raises the cost of the service [ 236 ]. Therefore, achieving an ideal resource allocation management is a complicated and challenging task as several objectives should be fulfilled together [ 209 ]. Hence, providing more efficient QoS management of functions by the auto-scaling is essential to be considered without degrading the fault-tolerance features and increasing the cost.

Pricing is crucial for both customers and cloud providers. However, there is a shortage in pricing models, as there is an imbalance in needs between serverless providers, developers, and service end-users [ 236 ]. The pricing scheme for most cloud providers is based on the number of functions’ requests and execution time-the quantity of consumed resources [ 123 , 200 ]. Currently, FaaS is less expensive when functions are bound to I/O than CPU. Moreover, services that dynamically adjust resource consumption are unable to predict the optical computing technology. It is crucial to implement solutions that offer cost-effective computing resources. FaStest reduced the cost by 50% via learning the behavioral pattern of functions using machine learning [ 202 ]. Price estimation has a great impact on selecting the most optimal provider. Therefore there should be more researches on developing tools to predict the pricing in advance.

Since the serverless emergence, researchers are working on the open question of how to decompose legacy systems into FaaS without degrading performance [ 208 ]. Several works have been done on migrating to FaaS [ 76 , 130 , 286 ]. The currently available automated tools for migrating legacy code into FaaS are not fully practical due to the remaining manual work that needs to be done [ 17 ]. Therefore, finding optimal automatic migration solutions for existing legacy systems is an interesting research direction [ 130 ]. Moreover, research on tools for checking whether a legacy system will fit the serverless paradigm is a crucial line. Also, developing and enhancing automatic and semi-automatic analysis strategies based on artificial intelligence could be another future research field.

Debugging, testing, and benchmarking

The available tools for testing, debugging, and deployment are immature, this prevents some developers from entering the serverless environment. The shortage of tools in FaaS is a core problem, particularly the testing tools [ 17 ]. Moreover, most FaaS environments lack powerful local emulation platforms for testing. Therefore, developers are mostly depending on the server-side, which is expensive. Developers need to be ensured about the adequate testing tools before diving into the serverless world. A challenging aspect in benchmarking is the lack of information due to the heterogeneity of the cloud provider data center: hardware, software, and configurations [ 287 ]. Additionally, benchmarking FaaS platforms should take advantage of analyzing the cloud services, which lacks limited accessible measurements and hidden modification of services over time [ 55 ]. Thus, it is essential to have transparent, fair, and standardized benchmarking tools available for developers.

Threats to validity

Several threats might impact the validity of the literature mapping studies. In this paper, popular instructions and guidelines were taken into account to avoid threats to validity as follows:

Coverage of research questions: All up-to-date research aspects of serverless computing might not be included in this study. To overcome this threat, the brainstorming was conducted by all the authors in determining the most current research questions in the area.

Coverage of related papers: The process of obtaining all the related studies in serverless computing cannot be secured. In this study, various literature databases were employed; moreover, the method based on different terms and synonymous is followed by all the authors in determining the related questions.

Paper inclusion/exclusion criteria: The individual bias and interpretation could affect the implementation of the criteria. Therefore, to solve this problem, the agreements of all authors were considered in excluding or including a paper.

Accuracy of data extraction: The individual experience effects extracting the data, therefore online meetings were conducted after the data extraction process by each author. During the meetings, the outcomes from each author were compared with other findings to determine the differences and reach a final consensus.

Reproducibility of the study: Whether other researchers could obtain similar outcomes of this study is another threat. Thus, to address this, the research methodology contains the well-explained steps and actions conducted in this paper (as shown in “ Research methodology ” section).

Conclusions

The contributions of the work presented in this paper are threefold: (a) a methodical review of related literature on the topic of serverless computing, to address the issue of the lack of compiling information on the state-of-the-art of the field; (b) a comparison of the platforms and tools used in serverless computing; (c) an extensive analysis of the differences, benefits, and issues related to serverless computing, to provide a more complete understanding of the topic. Given the fast evolution and growing interest in the field, this survey focused on gathering the most outstanding trends and outcomes of serverless computing, as described by recent researchers. This survey could significantly reduce ambiguity and the entry barrier for novice developers to adapt to the serverless environment. Furthermore, the findings presented in this study could be of great value for future researchers interested in further investigating serverless computing. Finally, it is worth mentioning that the interest that both commercial and academic efforts fueled into studying, developing, and implementing serverless tools in forthcoming years could help maximize the potential that serverless computing could bring to the IT community.

Availability of data and materials

Not applicable.

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Software Engineering and Embedded Systems (SEES) Research Group, College of Medicine, University of Duhok, Duhok, Kurdistan Region, Iraq

Hassan B. Hassan

Software Engineering and Embedded Systems (SEES) Research Group, Department of Computer Science, College of Science, University of Duhok, Duhok, Kurdistan Region, Iraq

Saman A. Barakat & Qusay I. Sarhan

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Conceptualization: HBH, SAB, and QIS; methodology: HBH, SAB, and QIS; validation: HBH, SAB, and QIS; formal analysis: HBH, SAB, and QIS; investigation: HBH, SAB, and QIS; resources: HBH; data curation, HBH and SAB; writing—original draft preparation: HBH, SAB, and QIS; writing—review and editing: HBH, SAB, and QIS; visualization: SAB; supervision: QIS; It is noted that all authors cooperated with each other to achieve suitable information flow across the entire paper. The authors read and approved the final manuscript.

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Hassan B. Hassan received the B.Sc. degree in Computer Science from University of Duhok, Iraq, in 2010. He completed the M.Sc. degree in Web Applications and Services, from Leicester University, UK, in 2015. He is currently working as an assistant lecturer at the college of medicine, University of Duhok, Iraq. His main areas of research interest are cloud computing, web programming, big data, and human computer interaction.

Saman A. Barakat received the B.Sc. degree in Computer Science from University of Duhok, Iraq, in 2008. He completed the M.Sc. degree in Advanced Computer Science, from Newcastle University, UK, in 2012. He is currently working as a lecturer at the college of science, University of Duhok, Iraq. His main areas of research interest are cloud computing, and software engineering.

Qusay I. Sarhan received the B.Sc. degree in Software Engineering from University of Mosul, Iraq, in 2007 and the M.Tech. degree in Software Engineering from Jawaharlal Nehru Technological University, India, in 2011. Currently, he is a lecturer and the leader of Software Engineering and Embedded Systems (SEES) research group at University of Duhok, Iraq. He has a couple of national and international publications and his research interests include software engineering, internet of things, and embedded systems.

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Correspondence to Qusay I. Sarhan .

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Hassan, H.B., Barakat, S.A. & Sarhan, Q.I. Survey on serverless computing. J Cloud Comp 10 , 39 (2021). https://doi.org/10.1186/s13677-021-00253-7

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Top 10 Cloud Computing Research Topics of 2024

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Cloud computing is a fast-growing area in the technical landscape due to its recent developments. If we look ahead to 2024, there are new research topics in cloud computing that are getting more traction among researchers and practitioners. Cloud computing has ranged from new evolutions on security and privacy with the use of AI & ML usage in the Cloud computing for the new cloud-based applications for specific domains or industries. In this article, we will investigate some of the top cloud computing research topics for 2024 and explore what we get most out of it for researchers or cloud practitioners. To master a cloud computing field, we need to check these Cloud Computing online courses .

Why Cloud Computing is Important for Data-driven Business?

The Cloud computing is crucial for data-driven businesses because it provides scalable and cost-effective ways to store and process huge amounts of data. Cloud-based storage and analytical platform helps business to easily access their data whenever required irrespective of where it is located physically. This helps businesses to take good decisions about their products and marketing plans. 

Cloud computing could help businesses to improve their security in terms of data, Cloud providers offer various features such as data encryption and access control to their customers so that they can protect the data as well as from unauthorized access. 

Few benefits of Cloud computing are listed below: 

  • Scalability: With Cloud computing we get scalable applications which suits for large scale production systems for Businesses which store and process large sets of data.
  • Cost-effectiveness : It is evident that Cloud computing is cost effective solution compared to the traditional on-premises data storage and analytical solutions due to its scaling capacity which leads to saving more IT costs. 
  • Security : Cloud providers offer various security features which includes data encryption and access control, that can help businesses to protect their data from unauthorized access.
  • Reliability : Cloud providers ensure high reliability to their customers based on their SLA which is useful for the data-driven business to operate 24X7. 

Top 10 Cloud Computing Research Topics

1. neural network based multi-objective evolutionary algorithm for dynamic workflow scheduling in cloud computing.

Cloud computing research topics are getting wider traction in the Cloud Computing field. These topics in the paper suggest a multi-objective evolutionary algorithm (NN-MOEA) based on neural networks for dynamic workflow scheduling in cloud computing. Due to the dynamic nature of cloud resources and the numerous competing objectives that need to be optimized, scheduling workflows in cloud computing is difficult. The NN-MOEA algorithm utilizes neural networks to optimize multiple objectives, such as planning, cost, and resource utilization. This research focuses on cloud computing and its potential to enhance the efficiency and effectiveness of businesses' cloud-based workflows.

The algorithm predicts workflow completion time using a feedforward neural network based on input and output data sizes and cloud resources. It generates a balanced schedule by taking into account conflicting objectives and projected execution time. It also includes an evolutionary algorithm for future improvement.

The proposed NN-MOEA algorithm has several benefits, such as the capacity to manage dynamic changes in cloud resources and the capacity to simultaneously optimize multiple objectives. The algorithm is also capable of handling a variety of workflows and is easily expandable to include additional goals. The algorithm's use of neural networks to forecast task execution times is a crucial component because it enables the algorithm to generate better schedules and more accurate predictions.

The paper concludes by presenting a novel multi-objective evolutionary algorithm-based neural network-based approach to dynamic workflow scheduling in cloud computing. In terms of optimizing multiple objectives, such as make span and cost, and achieving a better balance between them, these cloud computing dissertation topics on the proposed NN-MOEA algorithm exhibit encouraging results.

Key insights and Research Ideas:

Investigate the use of different neural network architectures for predicting the future positions of optimal solutions. Explore the use of different multi-objective evolutionary algorithms for solving dynamic workflow scheduling problems. Develop a cloud-based workflow scheduling platform that implements the proposed algorithm and makes it available to researchers and practitioners.

2. A systematic literature review on cloud computing security: threats and mitigation strategies 

This is one of cloud computing security research topics in the cloud computing paradigm. The authors then provide a systematic literature review of studies that address security threats to cloud computing and mitigation techniques and were published between 2010 and 2020. They list and classify the risks and defense mechanisms covered in the literature, as well as the frequency and distribution of these subjects over time.

The paper suggests the data breaches, Insider threats and DDoS attack are most discussed threats to the security of cloud computing. Identity and access management, encryption, and intrusion detection and prevention systems are the mitigation techniques that are most frequently discussed. Authors depict the future trends of machine learning and artificial intelligence might help cloud computing to mitigate its risks. 

The paper offers a thorough overview of security risks and mitigation techniques in cloud computing, and it emphasizes the need for more research and development in this field to address the constantly changing security issues with cloud computing. This research could help businesses to reduce the amount of spam that they receive in their cloud-based email systems.

Explore the use of blockchain technology to improve the security of cloud computing systems. Investigate the use of machine learning and artificial intelligence to detect and prevent cloud computing attacks. Develop new security tools and technologies for cloud computing environments. 

3. Spam Identification in Cloud Computing Based on Text Filtering System

A text filtering system is suggested in the paper "Spam Identification in Cloud Computing Based on Text Filtering System" to help identify spam emails in cloud computing environments. Spam emails are a significant issue in cloud computing because they can use up computing resources and jeopardize the system's security. 

To detect spam emails, the suggested system combines text filtering methods with machine learning algorithms. The email content is first pre-processed by the system, which eliminates stop words and stems the remaining words. The preprocessed text is then subjected to several filters, including a blacklist filter and a Bayesian filter, to identify spam emails.

In order to categorize emails as spam or non-spam based on their content, the system also employs machine learning algorithms like decision trees and random forests. The authors use a dataset of emails gathered from a cloud computing environment to train and test the system. They then assess its performance using metrics like precision, recall, and F1 score.

The findings demonstrate the effectiveness of the proposed system in detecting spam emails, achieving high precision and recall rates. By contrasting their system with other spam identification systems, the authors also show how accurate and effective it is. 

The method presented in the paper for locating spam emails in cloud computing environments has the potential to improve the overall security and performance of cloud computing systems. This is one of the interesting clouds computing current research topics to explore and innovate. This is one of the good Cloud computing research topics to protect the Mail threats. 

Create a stronger spam filtering system that can recognize spam emails even when they are made to avoid detection by more common spam filters. examine the application of artificial intelligence and machine learning to the evaluation of spam filtering system accuracy. Create a more effective spam filtering system that can handle a lot of emails quickly and accurately.

4. Blockchain data-based cloud data integrity protection mechanism 

The "Blockchain data-based cloud data integrity protection mechanism" paper suggests a method for safeguarding the integrity of cloud data and which is one of the Cloud computing research topics. In order to store and process massive amounts of data, cloud computing has grown in popularity, but issues with data security and integrity still exist. For the proposed mechanism to guarantee the availability and integrity of cloud data, data redundancy and blockchain technology are combined.

A data redundancy layer, a blockchain layer, and a verification and recovery layer make up the mechanism. For availability in the event of server failure, the data redundancy layer replicates the cloud data across multiple cloud servers. The blockchain layer stores the metadata (such as access rights) and hash values of the cloud data and access control information

Using a dataset of cloud data, the authors assess the performance of the suggested mechanism and compare it to other cloud data protection mechanisms. The findings demonstrate that the suggested mechanism offers high levels of data availability and integrity and is superior to other mechanisms in terms of processing speed and storage space.

Overall, the paper offers a promising strategy for using blockchain technology to guarantee the availability and integrity of cloud data. The suggested mechanism may assist in addressing cloud computing's security issues and enhancing the dependability of cloud data processing and storage. This research could help businesses to protect the integrity of their cloud-based data from unauthorized access and manipulation.

Create a data integrity protection system based on blockchain that is capable of detecting and preventing data tampering in cloud computing environments. For enhancing the functionality and scalability of blockchain-based data integrity protection mechanisms, look into the use of various blockchain consensus algorithms. Create a data integrity protection system based on blockchain that is compatible with current cloud computing platforms. Create a safe and private data integrity protection system based on blockchain technology.

5. A survey on internet of things and cloud computing for healthcare

This article suggests how recent tech trends like the Internet of Things (IoT) and cloud computing could transform the healthcare industry. It is one of the Cloud computing research topics. These emerging technologies open exciting possibilities by enabling remote patient monitoring, personalized care, and efficient data management. This topic is one of the IoT and cloud computing research papers which aims to share a wider range of information. 

The authors categorize the research into IoT-based systems, cloud-based systems, and integrated systems using both IoT and the cloud. They discussed the pros of real-time data collection, improved care coordination, automated diagnosis and treatment.

However, the authors also acknowledge concerns around data security, privacy, and the need for standardized protocols and platforms. Widespread adoption of these technologies faces challenges in ensuring they are implemented responsibly and ethically. To begin the journey KnowledgeHut’s Cloud Computing online course s are good starter for beginners so that they can cope with Cloud computing with IOT. 

Overall, the paper provides a comprehensive overview of this rapidly developing field, highlighting opportunities to revolutionize how healthcare is delivered. New devices, systems and data analytics powered by IoT, and cloud computing could enable more proactive, preventative and affordable care in the future. But careful planning and governance will be crucial to maximize the value of these technologies while mitigating risks to patient safety, trust and autonomy. This research could help businesses to explore the potential of IoT and cloud computing to improve healthcare delivery.

Examine how IoT and cloud computing are affecting patient outcomes in various healthcare settings, including hospitals, clinics, and home care. Analyze how well various IoT devices and cloud computing platforms perform in-the-moment patient data collection, archival, and analysis. assessing the security and privacy risks connected to IoT devices and cloud computing in the healthcare industry and developing mitigation strategies.

6. Targeted influence maximization based on cloud computing over big data in social networks

Big data in cloud computing research papers are having huge visibility in the industry. The paper "Targeted Influence Maximization based on Cloud Computing over Big Data in Social Networks" proposes a targeted influence maximization algorithm to identify the most influential users in a social network. Influence maximization is the process of identifying a group of users in a social network who can have a significant impact or spread information. 

A targeted influence maximization algorithm is suggested in the paper "Targeted Influence maximization based on Cloud Computing over Big Data in Social Networks" to find the most influential users in a social network. The process of finding a group of users in a social network who can make a significant impact or spread information is known as influence maximization.

Four steps make up the suggested algorithm: feature extraction, classification, influence maximization, and data preprocessing. The authors gather and preprocess social network data, such as user profiles and interaction data, during the data preprocessing stage. Using machine learning methods like text mining and sentiment analysis, they extract features from the data during the feature extraction stage. Overall, the paper offers a promising strategy for maximizing targeted influence using big data and Cloud computing research topics to look into. The suggested algorithm could assist companies and organizations in pinpointing their marketing or communication strategies to reach the most influential members of a social network.

Key insights and Research Ideas: 

Develop a cloud-based targeted influence maximization algorithm that can effectively identify and influence a small number of users in a social network to achieve a desired outcome. Investigate the use of different cloud computing platforms to improve the performance and scalability of cloud-based targeted influence maximization algorithms. Develop a cloud-based targeted influence maximization algorithm that is compatible with existing social network platforms. Design a cloud-based targeted influence maximization algorithm that is secure and privacy-preserving.

7. Security and privacy protection in cloud computing: Discussions and challenges

Cloud computing current research topics are getting traction, this is of such topic which provides an overview of the challenges and discussions surrounding security and privacy protection in cloud computing. The authors highlight the importance of protecting sensitive data in the cloud, with the potential risks and threats to data privacy and security. The article explores various security and privacy issues that arise in cloud computing, including data breaches, insider threats, and regulatory compliance.

The article explores challenges associated with implementing these security measures and highlights the need for effective risk management strategies. Azure Solution Architect Certification course is suitable for a person who needs to work on Azure cloud as an architect who will do system design with keep security in mind. 

Final take away of cloud computing thesis paper by an author points out by discussing some of the emerging trends in cloud security and privacy, including the use of artificial intelligence and machine learning to enhance security, and the emergence of new regulatory frameworks designed to protect data in the cloud and is one of the Cloud computing research topics to keep an eye in the security domain. 

Develop a more comprehensive security and privacy framework for cloud computing. Explore the options with machine learning and artificial intelligence to enhance the security and privacy of cloud computing. Develop more robust security and privacy mechanisms for cloud computing. Design security and privacy policies for cloud computing that are fair and transparent. Educate cloud users about security and privacy risks and best practices.

8. Intelligent task prediction and computation offloading based on mobile-edge cloud computing

This Cloud Computing thesis paper "Intelligent Task Prediction and Computation Offloading Based on Mobile-Edge Cloud Computing" proposes a task prediction and computation offloading mechanism to improve the performance of mobile applications under the umbrella of cloud computing research ideas.

An algorithm for offloading computations and a task prediction model makes up the two main parts of the suggested mechanism. Based on the mobile application's usage patterns, the task prediction model employs machine learning techniques to forecast its upcoming tasks. This prediction is to decide whether to execute a specific task locally on the mobile device or offload the computation of it to the cloud.

Using a dataset of mobile application usage patterns, the authors assess the performance of the suggested mechanism and compare it to other computation offloading mechanisms. The findings demonstrate that the suggested mechanism performs better in terms of energy usage, response time, and network usage.

The authors also go over the difficulties in putting the suggested mechanism into practice, including the need for real-time task prediction and the trade-off between offloading computation and network usage. Additionally, they outline future research directions for mobile-edge cloud computing applications, including the use of edge caching and the integration of blockchain technology for security and privacy. 

Overall, the paper offers a promising strategy for enhancing mobile application performance through mobile-edge cloud computing. The suggested mechanism might improve the user experience for mobile users while lowering the energy consumption and response time of mobile applications. These Cloud computing dissertation topic leads to many innovation ideas. 

Develop an accurate task prediction model considering mobile device and cloud dynamics. Explore machine learning and AI for efficient computation offloading. Create a robust framework for diverse tasks and scenarios. Design a secure, privacy-preserving computation offloading mechanism. Assess computation offloading effectiveness in real-world mobile apps.

9. Cloud Computing and Security: The Security Mechanism and Pillars of ERPs on Cloud Technology

Enterprise resource planning (ERP) systems are one of the Cloud computing research topics in particular face security challenges with cloud computing, and the paper "Cloud Computing and Security: The Security Mechanism and Pillars of ERPs on Cloud Technology" discusses these challenges and suggests a security mechanism and pillars for protecting ERP systems on cloud technology.

The authors begin by going over the benefits of ERP systems and cloud computing as well as the security issues with cloud computing, like data breaches and insider threats. They then go on to present a security framework for cloud-based ERP systems that is built around four pillars: access control, data encryption, data backup and recovery, and security monitoring. The access control pillar restricts user access, while the data encryption pillar secures sensitive data. Data backup and recovery involve backing up lost or failed data. Security monitoring continuously monitors the ERP system for threats. The authors also discuss interoperability challenges and the need for standardization in securing ERP systems on the cloud. They propose future research directions, such as applying machine learning and artificial intelligence to security analytics.

Overall, the paper outlines a thorough strategy for safeguarding ERP systems using cloud computing and emphasizes the significance of addressing security issues related to this technology. Organizations can protect their ERP systems and make sure the Security as well as privacy of their data by implementing these security pillars and mechanisms. 

Investigate the application of blockchain technology to enhance the security of cloud-based ERP systems. Look into the use of machine learning and artificial intelligence to identify and stop security threats in cloud-based ERP systems. Create fresh security measures that are intended only for cloud-based ERP systems. By more effectively managing access control and data encryption, cloud-based ERP systems can be made more secure. Inform ERP users about the security dangers that come with cloud-based ERP systems and how to avoid them.

10. Optimized data storage algorithm of IoT based on cloud computing in distributed system

The article proposes an optimized data storage algorithm for Internet of Things (IoT) devices which runs on cloud computing in a distributed system. In IoT apps, which normally generate huge amounts of data by various devices, the algorithm tries to increase the data storage and faster retrials of the same. 

The algorithm proposed includes three main components: Data Processing, Data Storage, and Data Retrieval. The Data Processing module preprocesses IoT device data by filtering or compressing it. The Data Storage module distributes the preprocessed data across cloud servers using partitioning and stores it in a distributed database. The Data Retrieval module efficiently retrieves stored data in response to user queries, minimizing data transmission and enhancing query efficiency. The authors evaluated the algorithm's performance using an IoT dataset and compared it to other storage and retrieval algorithms. Results show that the proposed algorithm surpasses others in terms of storage effectiveness, query response time, and network usage. 

They suggest future directions such as leveraging edge computing and blockchain technology for optimizing data storage and retrieval in IoT applications. In conclusion, the paper introduces a promising method to improve data archival and retrieval in distributed cloud based IoT applications, enhancing the effectiveness and scalability of IoT applications.

Create a data storage algorithm capable of storing and managing large amounts of IoT data efficiently. Examine the use of cloud computing to improve the performance and scalability of data storage algorithms for IoT. Create a secure and privacy-preserving data storage algorithm. Assess the performance and effectiveness of data storage algorithms for IoT in real-world applications.

How to Write a Perfect Research Paper?

  • Choose a topic: Select the topic which is interesting to you so that you can share things with the viewer seamlessly with good content. 
  • Do your research: Read books, articles, and websites on your topic. Take notes and gather evidence to support your arguments.
  • Write an outline: This will help you organize your thoughts and make sure your paper flows smoothly.
  • Start your paper: Start with an introduction that grabs the reader's attention. Then, state your thesis statement and support it with evidence from your research. Finally, write a conclusion that summarizes your main points.
  • Edit and proofread your paper. Make sure you check the grammatical errors and spelling mistakes. 

Cloud computing is a rapidly evolving area with more interesting research topics being getting traction by researchers and practitioners. Cloud providers have their research to make sure their customer data is secured and take care of their security which includes encryption algorithms, improved access control and mitigating DDoS – Deniel of Service attack etc., 

With the improvements in AI & ML, a few features developed to improve the performance, efficiency, and security of cloud computing systems. Some of the research topics in this area include developing new algorithms for resource allocation, optimizing cloud workflows, and detecting and mitigating cyberattacks.

Cloud computing is being used in industries such as healthcare, finance, and manufacturing. Some of the research topics in this area include developing new cloud-based medical imaging applications, building cloud-based financial trading platforms, and designing cloud-based manufacturing systems.

Frequently Asked Questions (FAQs)

Data security and privacy problems, vendor lock-in, complex cloud management, a lack of standardization, and the risk of service provider disruptions are all current issues in cloud computing. Because data is housed on third-party servers, data security and privacy are key considerations. Vendor lock-in makes transferring providers harder and increases reliance on a single one. Managing many cloud services complicates things. Lack of standardization causes interoperability problems and restricts workload mobility between providers. 

Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) are the cloud computing scenarios where industries focusing right now. 

The six major components of cloud infrastructure are compute, storage, networking, security, management and monitoring, and database. These components enable cloud-based processing and execution, data storage and retrieval, communication between components, security measures, management and monitoring of the infrastructure, and database services.  

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Improving cloud security model for web applications using hybrid encryption techniques

R esearch published in the International Journal of Internet Technology and Secured Transactions uses a hybrid approach to boosting the security of online applications, particularly within the realm of cloud computing. By merging two distinct techniques—homomorphic encryption and the squirrel search algorithm (SSA)—the study demonstrates a significant enhancement in the security of cloud computing models.

Homomorphic encryption is a form of encryption that allows mathematical operations to be performed on encrypted data without first having to decrypt data. This means that computations can be carried out on encrypted text to yield useful results that, when decrypted, match the results of the same operations as if they had been performed on the plain text.

The SSA is a nature-inspired optimization algorithm that mimics the dynamic foraging behavior of flying squirrels. It's classified as a metaheuristic algorithm, meaning it solves problems iteratively using randomness and guided search instead of using a conventional approach.

R.S. Kanakasabapathi and J.E. Judith of the Department of Computer Applications at the Noorul Islam Centre for Higher Education in Kumarcoil, India, hoped to boost cloud data storage systems using an advanced encryption technique. Encryption obviously plays a key role in safeguarding data from unauthorized access or breaches.

The team has assessed the effectiveness of their approach, measuring upload and download time and encryption and decryption time. They demonstrated that the hybrid approach outperforms the Rivest-Shamir-Adleman (RSA) and ECC-based cryptography.

Ultimately, minimizing encryption and decryption times while maximizing data protection and so ensuring the integrity and confidentiality of cloud-stored information is critical. Given that there are ongoing concerns surrounding the security of cloud computing and ever-expanding volumes of data being stored and processed in the cloud, innovative approaches are needed to safeguard that data as each wave of malicious actors comes to the fore who might compromise or illicitly access that data.

More information: R.S. Kanakasabapathi et al, Improving cloud security model for web applications using hybrid encryption techniques, International Journal of Internet Technology and Secured Transactions (2024). DOI: 10.1504/IJITST.2024.136677

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More From Forbes

3 steps to combat the steady uptick of rising cloud costs.

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Scott Sellers is the co-founder and CEO of Azul , with 30 years of experience as an entrepreneur and executive in the technology industry.

Cloud costs continue to rise despite nearly every business taking steps to optimize their spending and knowing the negative impact the cloud bill has on their bottom line. As evidence of this, the U.S. government's inflation index recently recorded the highest-ever reading for a technology category that includes cloud computing. These rising cloud costs are causing companies and services like IBM Cloud, Salesforce, ServiceNow and numerous others to announce price hikes.

Even more so, AWS is pushing for more cost-aware processes , suggesting that analyzing application code performance and profiling cloud resource usage can help uncover incremental optimizations that may lead to large savings at scale—and I agree.

Programming Languages And Cloud Costs

Azul’s State of Java Survey and Report showed that 95% of companies using Java have taken steps to reduce cloud costs in the last year, including the drastic action of repatriating some applications from the public cloud back to on-premise deployments. The potential of cloud costs weighing down $1 trillion of market cap has proven more than enough to get the attention of business leaders.

Many companies continue to overprovision cloud resources, paying for capacity that is not being used. According to the Azul survey, nearly 70% of companies say they are paying for cloud capacity that they are not using, and more than 40% say they use less than 60% of the public cloud compute they are paying for.

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Finding that sweet spot between taking action to control costs and eliminating cloud resources that you don’t use calls for a targeted strategy before things get too out of control. Here are three specific ways companies can manage their cloud bill without affecting application performance and customer experience.

1. Optimize your computing infrastructure.

Maximizing the speed and performance of applications while keeping computing infrastructure costs in check is key. There are both software and hardware approaches to optimize infrastructure. In the realm of software, faster code execution results in less computing power needed, leading to less infrastructure needed and a smaller cloud bill.

Companies should begin the optimization process by asking the following questions:

• Are we over-provisioning to address business requirements?

• Are we running code slower than it could go with re-architecture or using faster runtimes?

• Does workload inconsistency mean we need to provision more headroom?

• Are common services in our application and development platform not cost-efficient?

• Is the cost to serve increasing faster than our business growth?

• Are we not efficiently using what we pay for (i.e., what are our utilization levels)?

2. Embrace performance engineering.

According to Gartner, companies that adopt a software development strategy that embraces performance engineering ( a systematic approach to continuous application performance improvement) will be more successful in identifying and addressing issues in efficiency and throughput. Gartner suggests balancing three approaches as a part of a performance engineering strategy: ensure business continuity, improve user experience and control infrastructure costs.

Keeping a vigilant eye on using optimized software stacks allows teams to eliminate unused resources in the cloud. The key is to continuously right-size cloud compute infrastructure without sacrificing performance SLAs. Here are five key areas to focus on:

1. Developing applications with a focus on maintaining lean and efficient code.

2. Reducing trade-offs between performance and efficiency.

3. Driving down CPU usage with faster and more efficient code execution.

4. Reducing compute instance counts with improved consistency (i.e., avoid the need to over-provision).

5. Maintaining service levels at higher loads or improved performance at the same or lower CPU thresholds.

3. Implement FinOps practices.

Financial operations (FinOps) is more than an operational or management practice that shares the responsibility for cloud computing infrastructure across the organization. It’s a true culture shift in how teams make decisions, going beyond cost-cutting to making cloud spend more visible and predictable.

Companies should implement regular touchpoints between engineering, finance and operations to review budgets and spending, with an eye toward identifying optimization opportunities. Encourage a spirit of shared ownership by making cloud usage and costs visible throughout the company and by setting shared goals and incentives around cloud efficiency.

Ideally, with the right monitoring and regularly updated dashboards, developers within engineering know how changes to an application impact cloud cost. Similarly, your operations team better understands the cost impact of ongoing deployment changes. It’s all about providing granular, accurate and up-to-date cost information from finance to the engineering and operations teams to keep a lid on costs.

By eliminating the silos of finance, engineering and operations, the teams can work together to improve the visibility of costs to serve a given application or service, improve the customer experience and achieve better margins. There is a nice harmony between determining how much each application costs to run and managing and optimizing its performance.

Final Thoughts

With the cloud now an integral part of so many businesses’ modernization and transformation strategies, demand for cloud compute will inevitably increase. However, a few focused and proactive steps can provide a path for organizations to obtain great value from cloud deployments without unnecessary overspending.

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    cloud computing Latest Research Papers | ScienceGate cloud computing Recently Published Documents TOTAL DOCUMENTS 29628 (FIVE YEARS 8901) H-INDEX 161 (FIVE YEARS 27) Latest Documents Most Cited Documents Contributed Authors Related Sources Related Keywords

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    The Journal of Cloud Computing, Advances, Systems and Applications (JoCCASA) has been launched to offer a high quality journal geared entirely towards the research that will offer up future generations of Clouds. The journal publishes research that addresses the entire Cloud stack, and as relates Clouds to wider paradigms and topics.

  5. Next generation cloud computing: New trends and research directions

    The landscape of cloud computing has significantly changed over the last decade. Not only have more providers and service offerings crowded the space, but also cloud infrastructure that was traditionally limited to single provider data centers is now evolving.

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    Research Published on: 19 February 2024 Full Text PDF Multiple objectives dynamic VM placement for application service availability in cloud networks Ensuring application service availability is a critical aspect of delivering quality cloud computing services.

  7. Welcome to the new Journal of Cloud Computing by Springer

    Published: 10 September 2021 Welcome to the new Journal of Cloud Computing by Springer Chunming Rong & Zhiming Zhao Journal of Cloud Computing 10, Article number: 49 ( 2021 ) Cite this article 5587 Accesses 1 Altmetric Metrics

  8. (PDF) Research Paper on Cloud Computing

    Research Paper on Cloud Computing June 2021 Authors: Mrs Ashwini Sheth Sachin Shankar Bhosale I.C.S.COLLEGE OF ARTS COMMERCE AND SCIENCE KHED RATANGIRI Mr Harshad Kadam Asst Prof Abstract Cloud...

  9. A COMPARATIVE STUDY ON THREE SELECTIVE CLOUD PROVIDERS

    the similarities and differences between various elements of cloud computing but also to propose some topics to look into for further research. KEYWORDS Cloud Computing, Trending Cloud Providers, cloud Service feature. 1. INTRODUCTION Cloud Computing is being lauded as the next-generation shift that combines the internet and

  10. Research Agenda in Cloud Technologies

    This paper is the first systematic review of peer-reviewed academic research published in this field, and aims to provide an overview of the swiftly developing advances in the technical foundations of cloud computing and their research efforts. Structured along the technical aspects on the cloud agenda, we discuss lessons from related ...

  11. Proceedings of the 2023 ACM Symposium on Cloud Computing

    Proceedings of the 2023 ACM Symposium on Cloud Computing | ACM Conferences SoCC '23: Proceedings of the 2023 ACM Symposium on Cloud Computing October 2023 Read More 2023 Proceeding Publisher: Association for Computing Machinery New York NY United States Conference:

  12. A Systematic Literature Review on Cloud Computing Security: Threats and

    Cloud computing has become a widely exploited research area in academia and industry. Cloud computing benefits both cloud services providers (CSPs) and consumers. The security challenges associated with cloud computing have been widely studied in the literature. This systematic literature review (SLR) is aimed to review the existing research studies on cloud computing security, threats, and ...

  13. Cloud Computing Continuum Research Topics and Challenges. A Multi

    3 Cloud Computing Continuum Research Topics and Challenges. The following is a brief description of the initial set of research themes and challenges. While the primary source of the research challenges is the analysis of research venues, it has been complemented by inputs from context analysis, surveys, interviews, and research projects.

  14. PDF The Rise of Cloud Computing: National Bureau of Economic Research

    provided to businesses, households, and the government. This new way of accessing computing services—typically referred to as "the cloud" or "cloud computing"— represents the latest transition to a new computing platform—one in which computing is done on a network of off-site computing resources accessed through the Internet.1 As

  15. A Review Paper on Cloud Computing

    Cloud computing has taken its place all over the IT industries. It is an on-demand internet-based computing service that provides the maximum result with minimum resources cloud computing provides a service that does not require any physical close to the computer hardware. Cloud Computing is a product of grid, distributed, parallel, and ubiquitous computing. This paper introduces the concepts ...

  16. Cloud Computing: A Systematic Literature Review and Future Agenda

    The cloud literature is analyzed systematically from the management and business point of view. The review is limited with journal articles and papers published between 2014 and 2019. This ...

  17. (PDF) A COMPREHENSIVE STUDY ON CLOUD COMPUTING

    Abstract. Cloud computing is regarded as massively scalable, an on-demand configurable resources computing model and is one of the latest topics in the information sector. It offers the cloud ...

  18. Top 10 Cloud Computing Research Topics in 2020

    Top 10 Cloud Computing Research Topics in 2020 Read Cloud computing has suddenly seen a spike in employment opportunities around the globe with tech giants like Amazon, Google, and Microsoft hiring people for their cloud infrastructure.

  19. green cloud computing Latest Research Papers

    A new metaheuristic‐based method for solving the virtual machines migration problem in the green cloud computing Concurrency and Computation Practice and Experience 10.1002/cpe.6579

  20. Survey on serverless computing

    Serverless computing has gained importance over the last decade as an exciting new field, owing to its large influence in reducing costs, decreasing latency, improving scalability, and eliminating server-side management, to name a few. However, to date there is a lack of in-depth survey that would help developers and researchers better understand the significance of serverless computing in ...

  21. Top 10 Cloud Computing Research Topics of 2024

    9 Mins In this article Cloud computing is a fast-growing area in the technical landscape due to its recent developments. If we look ahead to 2024, there are new research topics in cloud computing that are getting more traction among researchers and practitioners.

  22. Cloud costs continue to rise in 2024

    U.S. government economic data and vendor research point to a pattern of rising cloud costs. The Bureau of Labor Statistics' Producer Price Index (PPI) for January, released last week, reported a 0.6% month-over-month increase in data processing and related services, a category that includes cloud computing. The year-over-year uptick stands at 3.7%.

  23. Improving cloud security model for web applications using hybrid ...

    By merging two distinct techniques—homomorphic encryption and the squirrel search algorithm (SSA)—the study demonstrates a significant enhancement in the security of cloud computing models.

  24. (PDF) Research and Development on Cloud Computing

    In this research paper we have discussed importance of cloud computing, history, and the latest technical advancements in depth. Also, this study aims to introduce and define Software as a Service.

  25. 3 Steps To Combat The Steady Uptick Of Rising Cloud Costs

    As evidence of this, the U.S. government's inflation index recently recorded the highest-ever reading for a technology category that includes cloud computing. These rising cloud costs are causing ...

  26. 12 Latest Cloud Computing Research Topics

    Cloud Computing is gaining so much popularity an demand in the market. It is getting implemented in many organizations very fast. One of the major barriers for the cloud is real and perceived lack of security. There are many Cloud Computing Research Topics, which can be further taken to get the fruitful output.. In this tutorial, we are going to discuss 12 latest Cloud Computing Research Topics.

  27. Gartner Emerging Technologies and Trends Impact Radar for 2024

    Use this year's Gartner Emerging Tech Impact Radar to: ☑️Enhance your competitive edge in the smart world ☑️Prioritize prevalent and impactful GenAI use cases that already deliver real value to users ☑️Balance stimulating growth and mitigating risk ☑️Identify relevant emerging technologies that support your strategic product roadmap Explore all 30 technologies and trends: www ...

  28. Educationary360 on Instagram: "A. A new partnership between the

    A new partnership between the University of California, Berkeley, and the KTH Royal Institute ..." Educationary360 on Instagram: "A. A new partnership between the University of California, Berkeley, and the KTH Royal Institute of Technology in Sweden will increase educational and research cooperation technology.