Systematic Mapping Study on Performance Scalability in Big Data on Cloud Using VM and Container

Abstract : In recent years, big data and cloud computing have gained importance in IT and business. These two technologies are becoming complementing in a way that the former requires large amount of storage and computation power, which are the key enabler technologies of Big Data; the latter, cloud computing, brings the opportunity to scale on-demand computation power and provides massive quantities of storage space. Until recently, the only technique used in computation resource utilization was based on the hypervisor, which is used to create the virtual machine. Nowadays, another technique, which claims better resource utilization, called “container” is becoming popular. This technique is otherwise known as “lightweight virtualization” since it creates completely isolated virtual environments on top of underlying operating systems. The main objective of this study is to clarify the research area concerned with performance issues using VM and container in big data on cloud, and to give a direction for future research.
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Communication dans un congrès
Lazaros Iliadis; Ilias Maglogiannis. 12th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2016, Thessaloniki, Greece. IFIP Advances in Information and Communication Technology, AICT-475, pp.634-641, 2016, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-319-44944-9_56〉
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Cansu Gokhan, Ziya Karakaya, Ali Yazici. Systematic Mapping Study on Performance Scalability in Big Data on Cloud Using VM and Container. Lazaros Iliadis; Ilias Maglogiannis. 12th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2016, Thessaloniki, Greece. IFIP Advances in Information and Communication Technology, AICT-475, pp.634-641, 2016, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-319-44944-9_56〉. 〈hal-01557613〉

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