Cloud Computing Service Providers Research Articles

A Comprehensive Comparison of IBM Cloud and Microsoft Azure

The ever-growing demand for scalability, agility, and cost-effectiveness has fuelled cloud computing to the forefront of modern IT infrastructure. According to Precedence Research, the global cloud computing market size was valued at USD 480 billion in 2022 and is expected to hit USD 2297.37 billion by 2032 with a registered CAGR of 17% from 2023 to 2032 [1]. The growth of cloud computing is driven by various factors such as the rapid evolution of technology and the globalization of businesses. Cloud computing is already embraced in various industries like Financial Services, Manufacturing, Healthcare, Retail, Education, and Architecture Engineering & Construction (AEC), where it streamlines operations, enhances collaboration, and revolutionizes processes. Cloud computing holds immense potential, especially in sectors like healthcare, real estate, tourism, and small and medium enterprises, where it will facilitate faster and more informed decision-making processes through data collection and analysis. Additionally, cloud technology is revolutionizing government sectors by driving cost savings, enhancing efficiency, and promoting transparency and citizen engagement. With the increasing number of cloud computing service providers such as IBM, Microsoft, Google, Amazon, and others, it becomes crucial to delve into their technical nuances. Understanding how each provider's offerings align with specific business requirements, technical specifications, and future scalability is paramount for organizations venturing into their cloud journey. The main purpose of this paper is to explore and evaluate the technical dimensions of IBM Cloud and Microsoft Azure and understand its future potential.

Cloud Computing-Based Virtual Learning Environments: Factors That Build Trust and Satisfaction Leading to Acceptance

Cloud computing-based virtual learning environments (CVLE) can help students with rural backgrounds improve their academic skill set. However, the teachers and students are not satisfied with this pedagogical model. This study empirically examines whether CVLE-related performance attributes, characteristics, and administrative factors can help students with a rural background build internal feelings of trust in CVLE and satisfaction with its use to manage knowledge. As a result, they will intend to use CVLE to become academically competitive. We employed a mixed-method research approach, collected data from 449 university students with a rural background in Hefei, China, and performed analyses using Smart-PLS. We employed the stimulus–organism–response theory. We considered and empirically confirmed that CVLE-related performance attributes (perceived ease of use and usefulness), characteristics (interactivity, omnipresence), and administrative factors (university support) are stimuli to build an organism (trust, satisfaction). This organism will, in response, results in students’ intention to use CVLE. We also examined the mediating role of trust and satisfaction between stimulus and response factors and found a significant mediation between 11 out of 14 mediating associations. This study has theoretical implications for researchers and practical implications for policymakers, government educational authorities, the administration of higher education institutes, and cloud computing service providers.

A Study Resource Optimization Techniques Based Job Scheduling in Cloud Computing

Cloud computing has revolutionized the way businesses and individuals utilize computing resources. It offers on-demand access to a vast pool of virtualized resources, such as processing power, storage, and networking, through the Internet. One of the key challenges in cloud computing is efficiently scheduling jobs to maximize resource utilization and minimize costs. Job scheduling in cloud computing involves allocating tasks or jobs to available resources in an optimal manner. The objective is to minimize job completion time, maximize resource utilization, and meet various performance metrics such as response time, throughput, and energy consumption. Resource optimization techniques play a crucial role in achieving these objectives. Resource optimization techniques aim to efficiently allocate resources to jobs, taking into account factors like resource availability, job priorities, and constraints. These techniques utilize various algorithms and optimization approaches to make intelligent decisions about resource allocation. Research on resource optimization techniques for job scheduling in cloud computing is of significant importance due to the following reasons: Efficient Resource Utilization: Cloud computing environments consist of a large number of resources that need to be utilized effectively to maximize cost savings and overall system performance. By optimizing job scheduling, researchers can develop algorithms and techniques that ensure efficient utilization of resources, leading to improved productivity and reduced costs. Performance Improvement: Job scheduling plays a crucial role in meeting performance metrics such as response time, throughput, and reliability. By designing intelligent scheduling algorithms, researchers can improve the overall system performance, leading to better user experience and customer satisfaction. Scalability: Cloud computing environments are highly scalable, allowing users to dynamically scale resources based on their needs. Effective job scheduling techniques enable efficient resource allocation and scaling, ensuring that the system can handle varying workloads without compromising performance. Energy Efficiency: Cloud data centres consume significant amounts of energy, and optimizing resource allocation can contribute to energy conservation. By scheduling jobs intelligently, researchers can reduce energy consumption, leading to environmental benefits and cost savings for cloud service providers. Quality of Service (QoS): Cloud computing service providers often have service-level agreements (SLAs) that define the QoS requirements expected by users. Resource optimization techniques for job scheduling can help meet these SLAs by ensuring that jobs are allocated resources in a timely manner, meeting performance guarantees, and maintaining high service availability. Here in this research, we have used the method of the weighted product model (WPM). For the topic of Resource Optimization Techniques Based Job Scheduling in Cloud Computing For calculating the values of alternative and evaluation parameters. A variation of the WSM called the weighted product method (WPM) has been proposed to address some of the weaknesses of The WSM that came before it. The main distinction is that the multiplication is being used in place of additional. The terms "scoring methods" are frequently used to describe WSM and WPM Execution time on Virtual machine, Transmission time (delay)on Virtual machine, Processing cost of a task on virtual machine resource optimization techniques based on job scheduling play a crucial role in maximizing the efficiency and performance of cloud computing systems. By effectively managing and allocating resources, these techniques help minimize costs, reduce energy consumption, and improve overall system throughput. One of the key findings is that intelligent job scheduling algorithms, such as genetic algorithms, ant colony optimization,

Exploring Cloud Computing Adoption in Higher Educational Environment: An Extension of the Tpb Model With Trust, Peer Influences, Perceived Usefulness and Ease of Use

Cloud computing is regarded as the next generation of computing. It is progressively being used as a launching pad for digital innovation and organizational agility. Cloud computing is frequently used by private and public organizations due to its flexibility, collaboration, cost-effectiveness, and scalability. These characteristics make cloud computing indispensable for individuals and businesses such as higher education institutes. Several prior studies covered the technological facets of cloud-based contexts, including cloud security, scalability, and virtualization. However, it is contend that the main barrier to cloud computing isn't technical but cognitive or behavioural, and in particular attitudinal. Thus, this research aims to study higher education’ students’ attitudes and their intention to adopt cloud computing, with a specific concentration on the effect of trust, peer influences, perceived usefulness and ease of use in order to investigate the factors influencing the adoption of cloud computing in higher educational environment in Saudi Arabia. This study presents an extended Decomposed Theory of Planned Behaviour (DTPB) to include trust, peer influences, perceived usefulness and ease of use as a cognition, representing a person’s perception of social influence to perform or not perform a behaviour under consideration. The proposed model was able to explain 62% of the variance in behavioural intention and 65% of students' attitudes towards the adoption of cloud computing in higher educational environment. The study's findings show that the proposed model explained a significant amount of variation in cloud computing adoption. It suggests that the model expansion by incorporating trust, peer influences, perceived usefulness and ease of use factors were valuable explorations. Further, the findings demonstrate that university students' attitudes toward using cloud computing are significantly influenced by perceived ease of use, trust in cloud computing service provider and perceived usefulness, which have the ability to explain their attitude by 22.15%, 21.9% and 20.9% respectively. The result also shows that "subjective norm" alone explains 33.95% of students' "behavioural intentions" towards using cloud computing, followed by their "attitude," which explains around 14.24% of "behavioural intentions," and then university students’ "self-efficacy," with 13.71%.

Cloud computing enabled business model innovation

Cloud computing's primary layer of security is its widespread adoption by businesses due to its ability to provide easy, on-demand network access to a large pool of configurable processing resources. Pressure from developed-world governments to keep up with the latest IT trends makes it challenging for colleges around the world to adopt cloud computing in engineering education. Although cloud computing as a concept has become more popular in recent times and quite a few studies have been conducted, the adoption and application level is still very low, particularly in developing countries. This study provides a realistic framework for gauging the sustainability of a company model. While digitalization is widely acknowledged to be crucial, the study's results reveal that the perceived available options for business model innovation depend not only on the value proposition itself but also on the position in the value network. The primary goal of this research is to create a cloud computing service provider business model that is built on circular economy principles and can guarantee the sustainable usage of cloud computing resources.

Accurate Prediction of Workloads and Resources With Multi-Head Attention and Hybrid LSTM for Cloud Data Centers

Currently, cloud computing service providers face big challenges in predicting large-scale workload and resource usage time series. Due to the difficulty in capturing nonlinear features, traditional forecasting methods usually fail to achieve high prediction performance for resource usage and workload sequences. Besides, there is much noise in original time series of resources and workloads. If these time series are not de-noised by smoothing algorithms, the prediction results can fail to meet the providers' requirements. To do so, this work proposes a hybrid prediction model named VAMBiG that integrates <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</u> ariational mode decomposition, an <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> daptive Savitzky-Golay (SG) filter, a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</u> ulti-head attention mechanism, <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Bi</u> directional and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</u> rid versions of Long and Short Term Memory (LSTM) networks. VAMBiG adopts a signal decomposition method named variational mode decomposition to decompose complex and non-linear original time series into low-frequency intrinsic mode functions. Then, it adopts an adaptive SG filter as a data pre-processing tool to eliminate noise and extreme points in such functions. Afterwards, it adopts bidirectional and grid LSTM networks to capture bidirectional features and dimension ones, respectively. Finally, it adopts a multi-head attention mechanism to explore importance of different data dimensions. VAMBiG aims to predict resource usage and workloads in highly variable traces in clouds. Extensive experimental results demonstrate that it achieves higher-accuracy prediction than several advanced prediction approaches with datasets from Google and Alibaba cluster traces.

Determinants of cloud computing integration and its impact on sustainable performance in SMEs: An empirical investigation using the SEM-ANN approach

Although extant literature has thoroughly investigated the incorporation of cloud computing services, examining their influence on sustainable performance, particularly at the organizational level, is insufficient. Consequently, the present research aims to assess the factors that impact the integration of cloud computing within small and medium-sized enterprises (SMEs) and its subsequent effects on environmental, financial, and social performance. The data were collected from 415 SMEs and were analyzed using a hybrid SEM-ANN approach. PLS-SEM results indicate that relative advantage, complexity, compatibility, top management support, cost reduction, and government support significantly affect cloud computing integration. This study also empirically demonstrated that SMEs could improve their financial, environmental, and social performance by integrating cloud computing services. ANN results show that complexity, with a normalized importance (NI) of 89.14%, is ranked the first among other factors affecting cloud computing integration in SMEs. This is followed by cost reduction (NI = 82.67%), government support (NI = 73.37%), compatibility (NI = 70.02%), top management support (NI = 52.43%), and relative advantage (NI = 48.72%). Theoretically, this study goes beyond examining the determinants affecting cloud computing integration by examining their impact on SMEs’ environmental, financial, and social performance in a comprehensive manner. The study also provides several practical implications for policymakers, SME managers, and cloud computing service providers.

A Best Approach to Load Balancing in Cloud Computing Security Using SSL in Iaas Infrastructure

Now a days there is low performance of cloud computing service provider cloud computing service provider allow users to hire a service on rent basis to use high cost software and hardware resources so now a days this feature are usable without suffering discontinue services and performance of the system the secure socket layer and associated security algorithm come with internet age and first introduce by Netscape but some problem found with it which is found by some researcher. Now a days cloud computing facility are face problem of performance degradation but we have to avoid such problem.in this paper we have to avoid such problem so we propose a solution on that so in existing system the commercial offering from the F5 Inc a new network equipment manufacturer introduced a product BIG-IP With in simultaneous approach high availability load balancing and secure data channel Key Words: Key words: Secure Socket Layer (SSL), Local TrafficManager (LTM), Access Policy Manager (APM), Infrastructure – as – a - Service (IaaS), Throughput

Investigation of Efficient Backup Tecniques To Reduce Late In Cloud Systems: A Modeling Study

From the formation stages of the information society to the present day, new developments have been experienced in many areas such as data formation, processing, storage and sharing of data in electronic environments. In this context, many new technological concepts have been included in our lives. When it comes to data storage environments, the concept of "Cloud Computing", which has been developing and increasing in popularity in recent years, comes to our minds. Cloud computing, with its known definition, is an internet-based technology service that provides access to the data stored on its servers at any time through internet access and allows users to benefit from the services offered by the system to the extent they want. While the advantages of cloud computing systems such as low cost, high performance, not requiring physical materials and being flexible can be mentioned, it is also emphasized that it brings many problems in terms of system. The most important of these problems; the lack of resources in cloud computing service providers, causing unpredictable delays in server response times. Based on all this information, this research is structured on the development of solution processes on these delays. In this sense, it is possible to consider three areas of cloud infrastructure; cloud computing, distributed storage and streaming communications. One of the first solutions that comes to mind is duplicating a task on multiple machines and waiting for the earliest copy to finish can reduce service latency; but intuitively, it costs additional computing resources and increases the queue load on servers. In this context, the effect of redundancy on the tail will be analyzed in the first part of the research. As a solution proposal, in this section, it will be discussed that service delays and queue load can be reduced by using the replication method, thus making the system more efficient. In a similar way, by requesting more than one copy of a file and waiting for any of them to arrive, the cloud storage requests will be accelerated for content download. In the second part of the research, generalization will be made from replication to coding and the (n, k) fork-join model will be studied to analyze the delay in accessing a storage system with (n, k) deletion code. This analysis will provide practical information that several users can access a content at the same time and provide faster service to the relevant users. Achieving low latency in streaming communication is even more difficult, as packets must be transmitted quickly and sequentially. Based on this structure, in the third part of the research, it is aimed to develop deletion codes to transmit redundant packet combinations and ensure smooth playback. Experimental modeling method was used as a research method in the study. In general, the aim of the research is to blend various mathematical tools from queuing, theory coding and renewal processes. It is foreseen that the techniques and insights to be developed with these dimensions of the research can be applied to other systems with stochastically changing components, and integrated studies can be made and contribute to the literature in this context.

Legal issues of consumer privacy protection in the cloud computing environment: analytic study in GDPR, and USA legislations

Cloud computing services are considered among the most important services provided for companies due to the various benefits they confer. However, data privacy is a major concern for users, and laws covering this contain many contradictions and require improvement. This paper discusses the laws governing privacy issues in cloud computing, highlights missing components that could be added to laws, and proposes amendments to laws that may help create a better consumer experience, improved service, and increased protection for personal data. At the end of the paper, a set of recommendations is proposed for governments and private companies that would increase the responsibility held by cloud computing service providers in the event of failing to protect personal data from privacy invasion.

Research on influencing factors of artificial intelligence multi-cloud scheduling applied talent training based on DEMATEL-TAISM

With the rapid development of Internet of Things (IoT) technology and the rising popularity of IoT devices, an increasing number of computing intensive IoT applications have been developed. However, due to the limited resources of IoT devices, cloud computing systems are required to compute intensive IoT applications. Furthermore, to be subject to a single cloud computing service provider, multi-cloud computing has become an IoT service cloud computing solution. As a result of the complexity of multi-cloud scheduling, the application of artificial intelligence is an important technology to solve IoT multi-cloud scheduling. The corresponding talent training plays an important role in the development and implementation of the IoT artificial intelligence multi-cloud scheduling. First, this paper studies the key influencing factors of IoT artificial intelligence multi-cloud scheduling applied talent training. Combined with the characteristics of the development of China’s artificial intelligence industry, this paper summarizes the influencing factors from the four-dimensional training path of government departments, universities, enterprises and scientific research institutes. The purpose of artificial intelligence multi-cloud scheduling applied talent training is to build an artificial intelligence multi-cloud scheduling applied talent training influencing factor index system. Then, the DEMATEL method is used to establish multiple correlation matrices according to the direct influence correlation between the factors and calculate the degree of influence, the degree of being influenced, the center degree and the cause degree of the factors. Using the improved AISM method, based on the idea of game confrontation, from the two opposite extraction rules of result priority and cause priority, a group of confrontation level topological maps with comprehensive influence values reflecting the interacting factors are obtained, and relevant suggestions are presented to provide a reference for the training of artificial intelligence multi-cloud scheduling applied talent.

SDN-Based Resource Allocation in Edge and Cloud Computing Systems: An Evolutionary Stackelberg Differential Game Approach

Recently, the boosting growth of computation-heavy applications raises great challenges for the Fifth Generation (5G) and future wireless networks. As responding, the hybrid edge and cloud computing (ECC) system has been expected as a promising solution to handle the increasing computational applications with low-latency and on-demand services of computation offloading, which requires new computing resource sharing and access control technology paradigms. This work establishes a software-defined networking (SDN) based architecture for edge/cloud computing services in 5G heterogeneous networks (HetNets), which can support efficient and on-demand computing resource management to optimize resource utilization and satisfy the time-varying computational tasks uploaded by user devices. In addition, resulting from the information incompleteness, we design an evolutionary game based service selection for users, which can model the replicator dynamics of service subscription. Based on this dynamic access model, a Stackelberg differential game based cloud computing resource sharing mechanism is proposed to facilitate the resource trading between the cloud computing service provider (CCP) and different edge computing service providers (ECPs). Then we derive the optimal pricing and allocation strategies of cloud computing resource based on the replicator dynamics of users’ service selection. These strategies can promise the maximum integral utilities to all computing service providers (CPs), meanwhile the user distribution can reach the evolutionary stable state at this Stackelberg equilibrium. Furthermore, simulation results validate the performance of the designed resource sharing mechanism, and reveal the convergence and equilibrium states of user selection, and computing resource pricing and allocation.

Artificial Intelligence Algorithm-Based Economic Denial of Sustainability Attack Detection Systems: Cloud Computing Environments.

Cloud computing is currently the most cost-effective means of providing commercial and consumer IT services online. However, it is prone to new flaws. An economic denial of sustainability attack (EDoS) specifically leverages the pay-per-use paradigm in building up resource demands over time, culminating in unanticipated usage charges to the cloud customer. We present an effective approach to mitigating EDoS attacks in cloud computing. To mitigate such distributed attacks, methods for detecting them on different cloud computing smart grids have been suggested. These include hard-threshold, machine, and deep learning, support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF) tree algorithms, namely convolutional neural network (CNN), and long short-term memory (LSTM). These algorithms have greater accuracies and lower false alarm rates and are essential for improving the cloud computing service provider security system. The dataset of nine injection attacks for testing machine and deep learning algorithms was obtained from the Cyber Range Lab at the University of New South Wales (UNSW), Canberra. The experiments were conducted in two categories: binary classification, which included normal and attack datasets, and multi-classification, which included nine classes of attack data. The results of the proposed algorithms showed that the RF approach achieved accuracy of 98% with binary classification, whereas the SVM model achieved accuracy of 97.54% with multi-classification. Moreover, statistical analyses, such as mean square error (MSE), Pearson correlation coefficient (R), and the root mean square error (RMSE), were applied in evaluating the prediction errors between the input data and the prediction values from different machine and deep learning algorithms. The RF tree algorithm achieved a very low prediction level (MSE = 0.01465) and a correlation R2 (R squared) level of 92.02% with the binary classification dataset, whereas the algorithm attained an R2 level of 89.35% with a multi-classification dataset. The findings of the proposed system were compared with different existing EDoS attack detection systems. The proposed attack mitigation algorithms, which were developed based on artificial intelligence, outperformed the few existing systems. The goal of this research is to enable the detection and effective mitigation of EDoS attacks.

Development and Utilization of College Chinese Curriculum Resources Based on Cloud Computing Resource Scheduling Algorithm

In the current situation of university education in China, there are some problems that need to be solved urgently in college Chinese teaching in China, which seriously restrict the realization and implementation of college Chinese teaching objectives. The emergence and development of cloud computing technology can integrate different information media, and through the integration of traditional text, pictures, videos, and other resource media, it can be presented in a clearer and more vivid way. In this paper, a cloud computing resource scheduling model is established for the realization of the platform for the development and utilization of university Chinese cloud computing curriculum resources. The multi-QoS (Quality of Service) objective constraint problem is transformed into a single objective constraint solving problem. The improved GA (genetic algorithm) is used to solve the single objective constraint problem, and the square of the objective function is used as a fitness function, and a better resource allocation strategy is obtained. Experimental results show that the allocation time of the algorithm proposed in this paper is greatly reduced, and the QoS guarantee ability of cloud computing service providers is effectively improved. It has certain reference value for the further research of cloud computing technology in the development and utilization of college Chinese curriculum resources.

BUSINESS PROCESS ANALYSIS OF CLOUD INCIDENT MANAGEMENT SERVICE WITH ACTIVITY ASSIGNMENT: A CASE OF PT. XYZ

PT. XYZ is one of the cloud computing service providers in Indonesia. XYZ company started banking and data center software business in 1987, in 2011, the company launched enterprise cloud computing services. This service provides virtual server infrastructure and combines it with network access. The increasing interest of customers who require cloud services allow disruptions to the resources which have been provided. As an experienced company, it has prepared for the disruptions with cloud service management which includes operational aspects of the services. To support the affected services, the company has a Standard Operating Procedure (SOP) which conforms with incident management. However, there are still problems that could potentially interfere with the incident handling process. This can be seen from the observations on the event log where there are indications that activities are not working properly. So, it is necessary to conduct business process analysis to optimize activities of incident management and end-to-end solutions to the customers. The research began with a literature review and data collection. Using the extraction of the event log data and the analysis process, it 806 cases and 5466 activities were found. Then the researchers conducted business process modeling from the event log to run process mining. From the results of process mining and the comparison with the SOP of PT. XYZ, it was found that the business process of incident handling was not implemented. And the average duration of incident handling was 3 to 4 days. This has an impact on the fees charged by PT. XYZ. The result of this research, by processing the data and analyzing the business processes, is knowing the implications for PT. XYZ. Based on the analysis, the researchers provide recommendations that the incident management must increase responsiveness between the service desk and each support team. The optimal incident management development needs to be formed by a functional team for incident monitoring and auto-closed system within a particular time.

Influence of Cloud Computing Adaption on Organization Performance: A Case Study of Selected Commercial Banks in Ilala Municipality

This study focuses on the influence of cloud computing adaption on organization performance with particular reference to selected Commercial Banks in Ilala Municipality. Specifically, the study aimed to assess how cloud computing increases efficiency of bank operations, to examine the effectiveness of cloud computing on enhancing customer satisfaction and to identify the challenges and opportunities encountering Commercial Banks in adopting and using cloud computing services. A sample of 70 respondents was drawn from the study population using simple random and purposive sampling techniques. Research questionnaire was applied in data collection and the collected data were analyzed using quantitative research techniques. Frequencies and percentages were computed to establish descriptive information of the collected data. Multiple regression statistical analysis with the help of Statistical Package for Social Science (SPSS) was employed to analyse the collected data. Results revealed that cloud computing is easy to use in banking operations, it shows that cloud computing in banks is accurate in data processing whereby to improve efficiency cloud technology can bring together multiple data and operational systems and increase efficiency in bank operations. Basing on the findings the study recommends that cloud computing service providers solve the issues that hamper the adoption of cloud computing. The government should create policies on cloud computing to be taught from schools so that people become aware of cloud computing. Seminars and events should be organized so that different kinds of individuals can be educated and informed about different ideas concerning cloud computing adaptation. The study recommends for further studies that cloud computing is a rapidly emerging technology in many companies. It would be interesting to concentrate on other industries that may have a greater demand for information management and so need the usage of cloud computing.

Effective Decision Making: Data Envelopment Analysis for Efficiency Evaluation in the Cloud Computing Marketplaces

Assessing business performance is a critical issue for practicing managers, and business performance has always been of interest to managers and researchers. In recent years, the world has experienced a rapid growth in the cloud computing service sector thanks to its benefits to business organizations and economic development. Therefore, the performance efficiency of this sector has been concerned as one of the keys in today’s economic environment. This study aimed to assess the performance efficiency of cloud computing service providers in the United States of America, one of the biggest global markets in terms of cloud computing, by applying the data envelopment analysis models. The efficiency of cloud computing providers was evaluated based on the assumption of the non-cooperative game among cloud computing providers in which providers selfishly choose the best strategy to maximize their payoff with three stages. In the first stage, the performance of these providers over the past period was measured by a super slack-based measure. In the second stage, the performance in the future period was predicted by the new data envelopment analysis model: the past–present–future model based on resampling. In the last stage, the efficiency improvement was investigated by adopting the Malmquist productivity index. The findings of this study indicated that the percentage of inefficient providers would increase from 10% in the period from 2017 to 2020 to 20% for 2021 and 2024. Moreover, 30% of providers showed a regress in performance efficiency over the research period of 2017 to 2024. The results of this study provide an insight picture to the decision-makers, and this research will fill the gap in the literature as the first study that measures and predicts the performance efficiency of cloud computing service providers, which will provide a helpful reference for future studies.

Providing impersonation resistance for biometric-based authentication scheme in mobile cloud computing service

Mobile cloud computing (MCC) is an emerging computing model with the growing demands of mobile devices by services diversity using mobile cloud technology. Authentication plays a fundamental role between the smart portable devices located in different places and the mobile cloud computing service providers in MCC. Several authors presented many authentication schemes using different cryptographic approaches for MCC in the past years. However, those schemes have not solved the impersonation attack well. To conquer this suffering and support more security functionalities, we propose an impersonation resistance for a biometric-based authentication scheme using hashing and symmetric parameter function in an MCC environment. The proposed scheme can provide mutual authentication and anonymity using the software AVISPA. The security of the proposed scheme is formally proved in the random oracle model. Regarding functionality and performance analysis, the comparative studies demonstrate that the design has a trade-off between resource consumption and security strengthens for MCC services. Therefore, the proposed scheme is more practical in MCC application.

Assessing the sustainability of cloud computing service providers for Industry 4.0: a state-of-the-art analytical approach

While interests in Industry 4.0 technologies such as cloud computing and the Internet of Things (IoT) are growing, an ongoing challenge is to properly evaluate the performance of the providers of such technologies. Methods have been developed and applied to assess the efficiency of cloud service providers (CSPs) for Industry 4.0. However, most existing methods suffer from such shortcomings as subjective weights and computing complexity, rendering it difficult to succinctly differentiate the performance of CSPs in a competitive market. Besides, the literature has not taken into account many different types of data such as ratios, integers, and undesirable factors that can affect the performance of CSPs. Most importantly, most existing studies do not consider the sustainability of CSPs in evaluating their performance. To address the above issues, we present a comprehensive analytical method based on data envelopment analysis (DEA) to gauge the sustainability of CSPs for Industry 4.0. Validating the usefulness of our method using a real-life dataset, we provide not only a viable means with sound academic underpinning but also significant managerial insights for practitioners to assess the sustainability of CSPs for Industry 4.0.

Comparison of cloud computing providers for development of big data and internet of things application

The improved technology of big data and the internet of things (IoT) increases the number of developments in the application of smart city and Industry 4.0. Thus, the need for high-performance cloud computing is increasing. However, the increase in cloud computing service providers causes difficulties in determining the chosen service provider. Therefore, the purpose of this study is to make comparisons to determine the criteria for selecting cloud computing services following the system architecture and services needed to develop IoT and big data applications. We have analyzed several parameters such as technology specifications, model services, data center location, big data service, internet of things, microservices architecture, cloud computing management, and machine learning. We use these parameters to compare several cloud computing service providers. The results present that the parameters able to use as a reference for choosing cloud computing for the implementation of IoT and big data technology.