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Horri, abbas, mosavi, Ali. (1402). A Multi-Dimensional Framework for Virtual Machine Consolidation. سامانه مدیریت نشریات علمی دانشگاه اصفهان, (), -. doi: 10.22108/jcs.2023.135300.1112
abbas Horri; Ali mosavi. "A Multi-Dimensional Framework for Virtual Machine Consolidation". سامانه مدیریت نشریات علمی دانشگاه اصفهان, , , 1402, -. doi: 10.22108/jcs.2023.135300.1112
Horri, abbas, mosavi, Ali. (1402). 'A Multi-Dimensional Framework for Virtual Machine Consolidation', سامانه مدیریت نشریات علمی دانشگاه اصفهان, (), pp. -. doi: 10.22108/jcs.2023.135300.1112
Horri, abbas, mosavi, Ali. A Multi-Dimensional Framework for Virtual Machine Consolidation. سامانه مدیریت نشریات علمی دانشگاه اصفهان, 1402; (): -. doi: 10.22108/jcs.2023.135300.1112

A Multi-Dimensional Framework for Virtual Machine Consolidation

Journal of Computing and Security
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 11 شهریور 1402
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22108/jcs.2023.135300.1112
نویسندگان
abbas Horri* 1؛ Ali mosavi2
1Computer Engineering Department, Faculty of Engineering, Shahrkord University, Shahrkord,Iran
2Database Department, faculty of Computer Science, Allamah University, Kabul, Afghanistan
چکیده
Cloud computing is a demand computing model that requires a large number of physical resources and provides services based on the request of each user. A large number of physical servers in data centers has high electrical energy consumption, which causes high operating cost, increases carbon dioxide (CO2) emission. The focus of this paper is on virtual machine consolidation to minimize power consumption.

This paper provides a framework that takes into account multi factors: CPU, memory, and bandwidth utilization in three terms: host overload detection, VM placement, and service level agreement violation. First, in the host overload detection term, we provide a Separately Local Regression Host Overload Detection (SLRHOD) algorithm that considers CPU, memory, and bandwidth utilization, separately. Second, in terms of VM placement which is an NP-hard problem, the Power Aware Best Fit Decreasing (PABFD) algorithm with consideration of Dot-product (DP) heuristics was proposed. Third CPU and memory take into account the calculation of SLA violation in terms of SLA violation.

Our simulation results show that our proposed algorithm outperforms the existing algorithm and improves energy consumption, SLA violations, and VM migrations.
کلیدواژه‌ها
Cloud Computing؛ Green Computing؛ Virtual Machine؛ Quality of Service؛ Energy Efficiency
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تعداد مشاهده مقاله: 25


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