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| 一种基于隐马尔可夫模型的虚拟机失效恢复方法 |
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张建华1,2,3, 张文博1, 徐继伟1,2,3, 魏峻1,2, 钟华1, 黄涛1,2
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1.中国科学院 软件研究所 软件工程技术中心, 北京 100190;2.计算机科学国家重点实验室(中国科学院 软件研究所), 北京 100190;3.中国科学院大学, 北京 100049
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| 摘要: |
| 随着虚拟化技术的发展与普及,越来越多的企业将关键业务系统部署到了虚拟化平台上.虚拟化技术降低了企业的硬件和管理成本,但同时也给系统的可靠性带来了严峻挑战.传统的方法通过运行时系统状态备份的方法来提高系统的失效恢复能力,但该方法会引入了巨大的系统开销.提出了一种基于隐马尔可夫模型的系统失效恢复性能优化方法.通过对系统运行时状态的预测分析,计算系统未来运行状态的概率趋势,并在运行过程中动态调整系统失效恢复功能与正常业务功能之间的资源分配,从而降低了系统的运行时性能开销,提高了业务系统服务能力.实验分析显示,该方法可以在保障系统可靠性的同时有效地降低系统的性能开销,在系统运行状态稳定的情况下,最高可以降低2/3的系统响应时间. |
| 关键词: 虚拟机 失效恢复 隐马尔可夫模型 预测 |
| DOI：10.13328/j.cnki.jos.004548 |
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| 基金项目:国家自然科学基金(61173003);国家高技术研究发展计划(863)(2012AA011204);国家科技支撑计划(2012BAH14B 02) |
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| Approach of Virtual Machine Failure Recovery Based on Hidden Markov Model |
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ZHANG Jian-Hua1,2,3, ZHANG Wen-Bo1, XU Ji-Wei1,2,3, WEI Jun1,2, ZHONG Hua1, HUANG Tao1,2
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1.Technology Center of Software Engineering, Institute of Software, The Chinese Academy of Sciences, Beijing 100190, China;2.State Key Laboratory of Computer Science (Institute of Software, The Chinese Academy of Sciences), Beijing 100190, China;3.University of Chinese Academy of Sciences, Beijing 100049, China
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| Abstract: |
| With the development and popularization of virtualization technology, more and more enterprises will deploy their business-critical systems on virtualization platform. While reducing the company's hardware and management costs, virtualization also brings severe challenges for system reliability. While the runtime system state replication backup method can improve the failure recovery capabilities of system, it also introduces huge overhead. This paper presents a performance optimization method based on hidden Markov model for system failure recovery. It analyzes runtime states of the system, and calculates the probability of system running tendency. Business system optimization is achieved by dynamically adjusting resources allocation between the failure recovery function and normal business function to reduce the runtime overhead. Experimental results show that the presented approach can guarantee reliability of the system while effectively reducing performance overhead by up to 2/3. |
| Key words: virtual machine failure recovery hidden Markov model prediction |
