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FuMAEVMS Novel VM Selection in Dynamic Consolidation of Virtual Machine
Background/Objective: Dynamic VM consolidation has been known as one from several ways to reduce energy consumption in cloud data center. The problem in reducing energy consumption with dynamic VM Consolidation has recognize as NP-Hard problem that divided into four subs, where VM selection is one of sub-problem. The objective VM selection is to choose the best VM that suitable to move from overload host and avoid oversubscription host. Methods: This research proposed novel VM selection model based on Fuzzy Logic, Markov Normal Algorithm (MA) and Existing VM Selection (FuMAEVMS) in dynamic VM consolidation. Moreover, RAM VM and max MIPS VM are consider as attribute to be used. The proposed VM selection model have been evaluated using Cloudsim with datasets from PlanetLabs in various condition of VM instance. The research measured the performance using several parameters such as Energy Consumption (EC), SLA Violation (SLAV), SLA Time per active host (SLATAH) and Performance Degradation Due Migration (PDM). FuMAEVMS were compared with existing VM selection such asconstant first selection (CFS), minimum migration time (MMT), random choice (RC) and maximum correlation (MC). Findings: FuMAEVMS as Novel VM selection capable to decide which VM that should be migrate from overload host in various condition VM instances that gives improvement of energy efficiency in cloud data center. We analyze the used of fuzzy logic in categorizing attribute VM capable to help in minimize rules production in Markov Normal Algorithm. This condition gives benefit to MA in deciding suitable existing VM selection such as CFS, MMT or MC to be used in migrating VM. Improvement: Results experiment has showed FuMAEVMS VM selection capable to reduced energy consumption in cloud data center significantly up to 4.45% when applied in dynamic VM consolidation compared with RC, MMT and MC.
CFS, Cloud Data Center, Energy Efficiency, Fuzzy, MC, Markov Normal Algorithm, MMT.
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