A systematic literature review of cloud computing in eHealth

Cloud computing in eHealth is an emerging area for only few years. There needs to identify the state of the art and pinpoint challenges and possible directions for researchers and applications developers. Based on this need, we have conducted a systematic review of cloud computing in eHealth. We searched ACM Digital Library, IEEE Xplore, Inspec, ISI Web of Science and Springer as well as relevant open-access journals for relevant articles. A total of 237 studies were first searched, of which 44 papers met the Include Criteria. The studies identified three types of studied areas about cloud computing in eHealth, namely (1) cloud-based eHealth framework design (n=13); (2) applications of cloud computing (n=17); and (3) security or privacy control mechanisms of healthcare data in the cloud (n=14). Most of the studies in the review were about designs and concept-proof. Only very few studies have evaluated their research in the real world, which may indicate that the application of cloud computing in eHealth is still very immature. However, our presented review could pinpoint that a hybrid cloud platform with mixed access control and security protection mechanisms will be a main research area for developing citizen centred home-based healthcare applications

On a Catalogue of Metrics for Evaluating Commercial Cloud Services

Given the continually increasing amount of commercial Cloud services in the market, evaluation of different services plays a significant role in cost-benefit analysis or decision making for choosing Cloud Computing. In particular, employing suitable metrics is essential in evaluation implementations. However, to the best of our knowledge, there is not any systematic discussion about metrics for evaluating Cloud services. By using the method of Systematic Literature Review (SLR), we have collected the de facto metrics adopted in the existing Cloud services evaluation work. The collected metrics were arranged following different Cloud service features to be evaluated, which essentially constructed an evaluation metrics catalogue, as shown in this paper. This metrics catalogue can be used to facilitate the future practice and research in the area of Cloud services evaluation. Moreover, considering metrics selection is a prerequisite of benchmark selection in evaluation implementations, this work also supplements the existing research in benchmarking the commercial Cloud services.Comment: 10 pages, Proceedings of the 13th ACM/IEEE International Conference on Grid Computing (Grid 2012), pp. 164-173, Beijing, China, September 20-23, 201

Towards a Taxonomy of Performance Evaluation of Commercial Cloud Services

Cloud Computing, as one of the most promising computing paradigms, has become increasingly accepted in industry. Numerous commercial providers have started to supply public Cloud services, and corresponding performance evaluation is then inevitably required for Cloud provider selection or cost-benefit analysis. Unfortunately, inaccurate and confusing evaluation implementations can be often seen in the context of commercial Cloud Computing, which could severely interfere and spoil evaluation-related comprehension and communication. This paper introduces a taxonomy to help profile and standardize the details of performance evaluation of commercial Cloud services. Through a systematic literature review, we constructed the taxonomy along two dimensions by arranging the atomic elements of Cloud-related performance evaluation. As such, this proposed taxonomy can be employed both to analyze existing evaluation practices through decomposition into elements and to design new experiments through composing elements for evaluating performance of commercial Cloud services. Moreover, through smooth expansion, we can continually adapt this taxonomy to the more general area of evaluation of Cloud Computing.Comment: 8 pages, Proceedings of the 5th International Conference on Cloud Computing (IEEE CLOUD 2012), pp. 344-351, Honolulu, Hawaii, USA, June 24-29, 201

The factors affecting on managing sensitive data in cloud computing

Cloud computing represents the most important shift in computing and information technology (IT). However, security and privacy remain the main obstacles to its widespread adoption. In this research we will review the security and privacy challenges that affect critical data in cloud computing and identify solutions that are used to address these challenges. Some questions that need answers are: (a) User access management, (b) Protect privacy of sensitive data, (c) Identity anonymity to protect the Identity of user and data file. To answer these questions, a systematic literature review was conducted and structured interview with several security experts working on cloud computing security to investigate the main objectives of proposed framework, a pilot study by using a structured questionnaire was conducted. Framework using multilevel to enhance management information system on sensitive data in cloud environment

A Literature Review on Cloud Computing Adoption Issues in Enterprises

Part 3: Creating Value through ApplicationsInternational audienceCloud computing has received increasing interest from enterprises since its inception. With its innovative information technology (IT) services delivery model, cloud computing could add technical and strategic business value to enterprises. However, cloud computing poses highly concerning internal (e.g., Top management and experience) and external issues (e.g., regulations and standards). This paper presents a systematic literature review to explore the current key issues related to cloud computing adoption. This is achieved by reviewing 51 articles published about cloud computing adoption. Using the grounded theory approach, articles are classified into eight main categories: internal, external, evaluation, proof of concept, adoption decision, implementation and integration, IT governance, and confirmation. Then, the eight categories are divided into two abstract categories: cloud computing adoption factors and processes, where the former affects the latter. The results of this review indicate that enterprises face serious issues before they decide to adopt cloud computing. Based on the findings, the paper provides a future information systems (IS) research agenda to explore the previously under-investigated areas regarding cloud computing adoption factors and processes. This paper calls for further theoretical, methodological, and empirical contributions to the research area of cloud computing adoption by enterprises

A systematic literature review of cloud computing adoption and impacts among Small Medium Enterprises (SMEs)

Although cloud computing is one of the most significant trends in information technology acquisition today, its adoption amongst the SMEs is still behind the larger conterparts. Additionally, among those that use, many face challenges to gain benefits as what is normally claimed. More research is needed to understand the issue. The purpose of this paper is to present the findings of a Systematic Literature Review (SLR) conducted related to cloud computing adoption among SMEs, particularly focusing on the post adoption stage. SLR method was employed as this method enable the review been done in a more comprehensive and rigorous manner. A total of 39 relevant articles were reviewed and the findings indicate that most past researches on cloud computing and SMEs focused on adoption, exploring factors that affect the adoption. Very few studies looked at the post adoption stage or the impacts of cloud computing on SMEs

Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI

Influenced by the great success of deep learning via cloud computing and the rapid development of edge chips, research in artificial intelligence (AI) has shifted to both of the computing paradigms, i.e., cloud computing and edge computing. In recent years, we have witnessed significant progress in developing more advanced AI models on cloud servers that surpass traditional deep learning models owing to model innovations (e.g., Transformers, Pretrained families), explosion of training data and soaring computing capabilities. However, edge computing, especially edge and cloud collaborative computing, are still in its infancy to announce their success due to the resource-constrained IoT scenarios with very limited algorithms deployed. In this survey, we conduct a systematic review for both cloud and edge AI. Specifically, we are the first to set up the collaborative learning mechanism for cloud and edge modeling with a thorough review of the architectures that enable such mechanism. We also discuss potentials and practical experiences of some on-going advanced edge AI topics including pretraining models, graph neural networks and reinforcement learning. Finally, we discuss the promising directions and challenges in this field.Comment: 20 pages, Transactions on Knowledge and Data Engineerin

Ontologies in Cloud Computing - Review and Future Directions

Cloud computing as a technology has the capacity to enhance cooperation, scalability, accessibility, and offers discount prospects using improved and effective computing, and this capability helps organizations to stay focused. Ontologies are used to model knowledge. Once knowledge is modeled, knowledge management systems can be used to search, match, visualize knowledge, and also infer new knowledge. Ontologies use semantic analysis to define information within an environment with interconnecting relationships between heterogeneous sets. This paper aims to provide a comprehensive review of the existing literature on ontology in cloud computing and defines the state of the art. We applied the systematic literature review (SLR) approach and identified 400 articles; 58 of the articles were selected after further selection based on set selection criteria, and 35 articles were considered relevant to the study. The study shows that four predominant areas of cloud computing—cloud security, cloud interoperability, cloud resources and service description, and cloud services discovery and selection—have attracted the attention of researchers as dominant areas where cloud ontologies have made great impact. The proposed methods in the literature applied 30 ontologies in the cloud domain, and five of the methods are still practiced in the legacy computing environment. From the analysis, it was found that several challenges exist, including those related to the application of ontologies to enhance business operations in the cloud and multi-cloud. Based on this review, the study summarizes some unresolved challenges and possible future directions for cloud ontology researchers.publishedVersio

Antecedents of Cloud Computing Adoption in the Malaysian context: A Systematic Literature Review

In a competitive marketplace, the competitiveness and survival of any corporation are often attributed to its ability to adopt innovative technology which bestows a competitive edge and reduced costs, improves the quality and the efficiency of its business processes. Cloud computing is a platform for the development of computational solutions for multiple fields of knowledge, as it offers cost-saving mechanisms and increased efficiency to organisations. Despite its maturity and enhancement, reviews pertaining to antecedents of cloud computing adoption in the Malaysian context are scarce. Thus, a systematic literature review using Scopus database for retrieving the related articles was carried out to shed light on the antecedents of cloud computing adoption in the Malaysian context. The review revealed that the antecedents influencing cloud computing adoption include technological context elements (relative advantages, technological readiness, cost-saving, and compatibility), organisational context elements (top management support), and environmental context elements (competitive pressure, external support/trading partner support/regulatory support, vendor reputation and trust). Understanding the antecedents of cloud computing adoption is crucial towards strengthening cloud computing adoption and, in turn, will improve the performance and competitiveness of corporate sectors. &nbsp