An Energy-Efficient Dynamic Resource Management Approach Based on Clustering and Meta-Heuristic Algorithms in Cloud Computing IaaS Platforms
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Cloud computing as an emerging technology, has revolutionized the information technology industry by elastic on-demand provisioning and De-provisioning of computing resources. Due to the huge amount of electrical energy consumption by large-scale Datacenters, it is essential to investigate various approaches in order to decrease simultaneously energy and its impacts on global economic crisis and ecological concerns. This study through virtualization technique applied a hybrid technique for resource management. This technique used k-means clustering for mapping task and dynamic consolidation method, which improved by micro-genetic algorithm. Experimental evaluation performed on CloudSim 3.0.3 and the results were analyzed with Expert-Choice software tools. We found that the proposed KMGA technique could provide a good trade-off between effectively reduce energy consumption of Datacenters and sustained quality of service. In addition, it minimized the number of virtual machine migrations and make-span, in comparison with particle swarm optimization and genetic algorithms in similar hybrid techniques.
KeywordsDynamic virtual machine consolidation Green cloud computing k-means clustering Micro-genetic algorithm Resource management
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