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:2019,28(3):158-164
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改进指数平滑预测的虚拟机自适应迁移策略
(太原科技大学 计算机科学与技术学院, 太原 030024)
Virtual Machine Adaptive Migration Strategy Based on Improved Exponential Smoothing Prediction
(Department of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, China)
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投稿时间:2018-09-12    修订日期:2018-10-12
中文摘要: 针对云数据中心虚拟机频繁迁移问题对虚拟机迁移时机进行研究,提出一种基于改进指数平滑预测的虚拟机自适应迁移策略.该策略采用双阈值和预测相结合的方法,连续判断负载状态触发负载预测,然后,根据历史负载值自适应地预测下一时刻主机负载状态并触发虚拟机迁移,实现主机负载平衡,提高迁移效率,降低能耗.经实验表明,该方法在能耗和虚拟机迁移次数方面分别可降低约7.34%和58.55%,具有良好的优化效果.
Abstract:In this work, the migration timing of virtual machines is studied for frequent migration of virtual machine in cloud data centers, an adaptive migration trigger method of virtual machine based on improved exponential smoothing prediction is proposed. A combination of dual threshold and prediction is applied to the strategy. First, the load prediction is triggered by continuously determining the load state. Then, the host load state at the next moment is adaptively predicted based on the historical load value, and finally the virtual machine migration is triggered. This method not only achieves host load balancing, but also improves migration efficiency and reduces energy consumption. Experiments show that the method reduces the energy consumption and the number of migration by about 7.34% and 58.55% respectively, which has sound optimization effect.
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基金项目:国家自然科学基金(61472269);山西省重点研发计划(高新领域)(201703D121042-1)
引用文本:
刘春霞,王娜,党伟超,白尚旺.改进指数平滑预测的虚拟机自适应迁移策略.计算机系统应用,2019,28(3):158-164
LIU Chun-Xia,WANG Na,DANG Wei-Chao,BAI Shang-Wang.Virtual Machine Adaptive Migration Strategy Based on Improved Exponential Smoothing Prediction.COMPUTER SYSTEMS APPLICATIONS,2019,28(3):158-164

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