基于动态双阈值增强搜索和救援优化的多目标节能虚拟机整合

Sweta Singh, R. Kumar, U. P. Rao
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引用次数: 0

摘要

云的普及及其解决复杂工程应用的需要引起了研究人员对能源和环境的关注。实现能源效率已经成为数据中心最基本的目标之一,以最小的能源消耗(EC)提供更多的服务。虚拟机整合的目的是通过动态迁移虚拟机,将虚拟机调整为更少的服务器,然后关闭不活跃的服务器,从而达到节能的目的。然而,不受控制的合并可能违反SLA。本文考虑了以EC为目标的优化问题和虚拟机迁移的数量。动态双阈值与增强搜索和救援(DDT-ESAR)优化引入了利用两个阈值;第一个值定义了主机分类的上界和下界,另一个值用于迁移决策。对于迁移,ESAR已被用于最合适的PM- VM映射。实验分析表明,计算得到的EC为0.384kWh, SLA违例率为6.33%,迁移次数为64次的效率。
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Multi-Objective Energy-Efficient Virtual Machine Consolidation Using Dynamic Double Threshold-Enhanced Search and Rescue-Based Optimization
The popularization of the cloud and its need to solve complex engineering application have alarmed energy and environmental concerns among the researchers. Achieving energy efficiency has become one of the most essential aims of the data center, offering more services with minimal energy consumption (EC). VM consolidation aims at adjusting the VMs to fewer PMs by live migration of VMs and then switching off the inactive servers, achieving energy efficiency. However, uncontrolled consolidation could violate the SLA. The paper contributes by considering the optimization problem targeting the EC and the number of VM migrations. Dynamic double threshold with enhanced search and rescue (DDT-ESAR) optimization has been introduced utilizing two thresholds; the first value defines the upper and lower bound for host classification, whereas the other is used to make migration decision. For migration, ESAR has been adopted for the most appropriate PM- VM mapping. The experimental analysis proves the efficiency where EC is computed to be 0.384kWh, SLA violations to be 6.33% and 64 number of migrations.
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