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This study aims to develop a scale to measure the cloud provider’s performance and it investigates the factors that impact that performance from the users’ perspective.

This paper proposes a research framework, develops hypotheses and conducts a survey to test the framework.

The results from both ordinary least square regression and structural equation modeling analyzes indicate that information technology complexity negatively and significantly affects users’ perception of the cloud computing providers’ performance. Additionally, the trust in the supervisor significantly enhances the otherwise insignificant positive relationship between providers’ cybersecurity capability and users’ perception of their providers’ performance.

The research makes important contributions to the cloud computing literature, as it measures users’ perception of the cloud computing provider’s performance and links it with cybersecurity, technical complexity and incorporates both the trust in the client firm’s supervisor and the strength of cybersecurity offered by cloud computing provider.

The purpose of this paper is to give a brief guidance on what a cloud provider should consider and what further actions to take to comply with General Data Protection Regulation (GDPR).

This paper presents in detail the requirements for GDPR compliance of cloud computing environments, presents the GDPR roles (data controller and data processor) in a cloud environment and discusses the applicability of GDPR compliance requirements for each cloud architecture (Infrastructure as a Service, Platform as a Service, Software as a Service), proposes countermeasures for satisfying the aforementioned requirements and demonstrates the applicability of the aforementioned requirements and countermeasures to a PaaS environment offering services for building, testing, deploying and managing applications through cloud managed data centers. The applicability of the method has been demonstrated on in a PaaS environment that offers services for building, testing, deploying and managing applications through cloud managed data centers.

The results of the proposed GDPR compliance measures for cloud providers highlight the effort and criticality required from cloud providers to achieve compliance.

As the storage and processing requirement of digital information is increasing on the cloud, it is very difficult for the single cloud provider (CP) to meet the resource requirement. Multiple providers form a federation for the execution of users’ requests. For the federated cloud, this paper aims to address the issue distribution of users’ request for resources and revenue among the providers by offering fair and stable distribution models for the federated cloud.

This paper uses cooperative game (CG)-theoretical models, i.e. Shapley–Shubik power index (SSPI) and Banzhaf power index (BPI) for distribution. Performance is analysed using variance and monotonicity using a case study.

Numerical analysis is done using two scenarios. Monotonicity is evaluated. Results show that SSPI performs better as compared to BPI in terms of fairness accuracy and the framework provide the fair distribution of revenue among providers in the federated cloud.

The proposed framework works efficiently under the specific defined conditions.

Paper provides the fair distribution. It assist the centralised cloud exchange in managing the users’ request in such a way every CPs, in the federated cloud will get an equal chance of serving the users’ request. The framework also provides the stable federation. Proposed work provides less rejection rate of users’ request. Finally, it assists the providers in increasing their profits in the federation.

This paper presents a CG theoretic-based framework for the distribution of resources required and revenue. The framework analysed the performance of distribution models by considering the variance and monotonicity for multiple users’ requests.

By introducing the mass customization service mode into the cloud logistics environment, this paper studies the joint optimization of service provider selection and customer order decoupling point (CODP) positioning based on the mass customization service mode to provide customers with more diversified and personalized service content with lower total logistics service cost.

This paper addresses the general process of service composition optimization based on the mass customization mode in a cloud logistics service environment and constructs a joint decision model for service provider selection and CODP positioning. In the model, the two objective functions of minimum service cost and most satisfactory delivery time are considered, and the Pareto optimal solution of the model is obtained via the NSGA-II algorithm. Then, a numerical case is used to verify the superiority of the service composition scheme based on the mass customization mode over the general scheme and to verify the significant impact of the scale effect coefficient on the optimal CODP location.

(1) Under the cloud logistics mode, the implementation of the logistics service mode based on mass customization can not only reduce the total cost of logistics services by means of the scale effect of massive orders on the cloud platform but also make more efficient use of a large number of logistics service providers gathered on the cloud platform to provide customers with more customized and diversified service content. (2) The scale effect coefficient directly affects the total cost of logistics services and significantly affects the location of the CODP. Therefore, before implementing the mass customization logistics service mode, the most reasonable clustering of orders on the cloud logistics platform is very important for the follow-up service combination.

The originality of this paper includes two aspects. One is to introduce the mass customization mode in the cloud logistics service environment for the first time and summarize the operation process of implementing the mass customization mode in the cloud logistics environment. Second, in order to solve the joint decision optimization model of provider selection and CODP positioning, this paper designs a method for solving a mixed-integer nonlinear programming model using a multi-layer coding genetic algorithm.

To effectively develop privacy policies and practices for cloud computing, organizations need to define a set of guiding privacy objectives that can be applied across their organization. It is argued that it is important to understand individuals’ privacy values with respect to cloud computing to define cloud privacy objectives.

For the purpose of this study, the authors adopted Keeney’s (1994) value-focused thinking approach to identify privacy objectives with respect to cloud computing.

The results of this study identified the following six fundamental cloud privacy objectives: to increase trust with cloud provider, to maximize identity management controls, to maximize responsibility of information stewardship, to maximize individual’s understanding of cloud service functionality, to maximize protection of rights to privacy, and to maintain the integrity of data.

One limitation is generalizability of the cloud privacy objectives, and the second is research bias. As this study focused on cloud privacy, the authors felt that the research participants’ increased knowledge of technology usage, including that of cloud technology, was a benefit that outweighed risks associated with not having a random selection of the general population. The newness and unique qualities of privacy issues in cloud computing are better fitted to a qualitative study where issues can emerge naturally through a holistic approach opposed to trying to force fit an existing set of variables or constructs into the context of privacy and cloud computing.

The findings of this research study can be used to assist management in the process of formulating a cloud privacy policy, develop cloud privacy evaluation criteria as well as assist auditors in developing their privacy audit work plans.

Currently, there is little to no guidance in the literature or in practice as to what organizations need to do to ensure they protect their stakeholders privacy in a cloud computing environment. This study works at closing this knowledge gap by identifying cloud privacy objectives.

Cloud computing provides a dynamic, heterogeneous and elastic environment by offering accessible ‘cloud services’ to end-users. The tasks involved in making cloud services available, such as matchmaking, selection and composition, are essential and closely related to each other. Integration of these tasks is critical for optimal composition and performance of the cloud service platform. More efficient solutions could be developed by considering cloud service tasks collectively, but the research and academic community have so far only considered these tasks individually. The purpose of this paper is to propose an integrated QoS-based approach for cloud service matchmaking, selection and composition using the Semantic Web.

In this paper, the authors propose a new approach using the Semantic Web and quality of service (QoS) model to perform cloud service matchmaking, selection and composition, to fulfil the requirements of an end user. In the Semantic Web, the authors develop cloud ontologies to provide semantic descriptions to the service provider and requester, so as to automate the cloud service tasks. This paper considers QoS parameters, such as availability, throughput, response time and cost, for quality assurance and enhanced user satisfaction.

This paper focus on the development of an integrated framework and approach for cloud service life cycle phases, such as discovery, selection and composition using QoS, to enhance user satisfaction and the Semantic Web, to achieve automation. To evaluate performance and usefulness, this paper uses a scenario based on a Healthcare Decision-Making System (HDMS). Results derived through the experiment prove that the proposed prototype performs well for the defined set of cloud-services tasks.

As a novel concept, our proposed integrated framework and approach for cloud service matchmaking, selection and composition based on the Semantic Web and QoS characterisitcs (availability, response time, throughput and cost), as part of the service level agreement (SLA) will help the end user to match, select and filter cloud services and integrate cloud-service providers into a multi-cloud environment.

The purpose of this paper is to analyze the influence of Technology Providers, Public Administrations and R&D Institutions on Cloud Computing adoption. This research also considers Killer Applications and Success Cases as other environmental factors.

Factorial analyses and structural equation models were used on a sample of high-technology firms located in technological parks in Southern Europe, with more than ten employees and sustained investments in R&D.

Results show that Technology Providers and Success Cases are determinant in Cloud Computing adoption. Moreover, Killer Applications are a forerunner for Success Cases.

An appropriate fit between the tools and resources provided by suppliers and the internal resources of the company is needed to create competitive advantages. Firms should evaluate Technology Providers, identify Success Cases to Cloud Computing adoption and implement technological benchmarking.

This study contributes to Cloud Computing adoption literature because it includes Technology Providers, Public Administrations and R&D Institutions simultaneously as well as other variables as Killer Applications and Success Cases. The importance of the external agents on information technology (IT) adoption, especially when the technologies to be adopted are new and in an emergent stage, together with the lack of prior investigations focusing on specific environmental factors affecting the adoption of these new, emerging IT, justify the value of this research.

This study aims to argue that user’s continued use behavior is contingent upon two perceptions (i.e. the app and the provider). This study examines the moderating effects of user’s perceptions of apps and providers on the effects of security and privacy concerns and investigate whether assurance mechanisms decrease such concerns.

This study conducts a scenario-based survey with 694 mobile cloud computing (MCC) app users to understand their perceptions and behaviors.

This study finds that while perceived value of data transfer to the cloud moderates the effects of security and privacy concerns on continued use behavior, trust only moderates the effect of privacy concerns. This study also finds that perceived effectiveness of security and privacy intervention impacts privacy concerns but does not decrease security concerns.

Prior mobile app studies mainly focused on mobile apps and did not investigate the perceptions of app providers along with app features in the same study. Furthermore, International Organization for Standardization 27018 certification and privacy policy notification are the interventions that exhibit data assurance mechanisms. However, it is unknown whether these interventions are able to decrease users’ security and privacy concerns after using MCC apps.

The COVID-19 pandemic has disrupted the ‘real’ world and substantially impacted the virtual world and thus the Internet ecosystem. It has caused a significant exogenous shock that offers a wealth of natural experiments and produced new data about broadband, clouds, and the Internet in times of crisis. In this chapter, we characterise and evaluate the evolving impact of the global COVID-19 crisis on traffic patterns and loads and the impact of those on Internet performance from multiple perspectives. While we place a particular focus on deriving insights into how we can better respond to crises and better plan for the post-COVID-19 ‘new normal’, we analyse the impact on and the responses by different actors of the Internet ecosystem across different jurisdictions. With a focus on the USA and Europe, we examine the responses of both public and private actors, with the latter including content and cloud providers, content delivery networks, and Internet service providers (ISPs). This chapter makes two contributions: first, we derive lessons learned for a future post-COVID-19 world to inform non-networking spheres and policy-making; second, the insights gained assist the networking community in better planning for the future.

This chapter reviews possible regulatory updates needed to address the four general challenges arising from digitalization of financial services, regardless of the business models of the financial services providers. These challenges are customers' data rights, artificial intelligence (AI) ethics, cybersecurity and financial exclusion.