支持大规模云服务平台的敏捷弹性伸缩技术  

Agile auto scaling for supporting large scale cloud service platform

作　　者：杨勇[1] 赵新奎[1] 卢兴见[1] 尹建伟[1] 

机构地区：[1]浙江大学计算机学院,浙江杭州310027

出　　处：《华中科技大学学报（自然科学版）》2013年第S2期63-67,99,共6页Journal of Huazhong University of Science and Technology(Natural Science Edition)

基　　金：国家科技支撑计划资助项目(2012BAH06F02);国家自然科学基金资助项目(61272129);教育部博士学科点专项科研基金资助项目(20110101110066);教育部优秀人才计划资助项目(NCET-12-0491);浙江省杰出青年科学基金资助项目(R13F020004)

摘　　要：针对当前大多数弹性伸缩算法存在粒度大、延迟高、开销大等问题,利用缓存思想引入挂起虚拟机,加快虚拟机供给速度;基于自回归滑动平均模型(ARMA)采用二级预测方法设计了敏捷弹性伸缩算法,实现了细粒度的资源分配;通过引入分位数统计、额外资源分配、资源延迟释放等策略,进一步保障服务质量(QoS).采用网易云阅读负载数据进行实验,结果表明:该算法能够进一步节省资源,同时使得服务质量提升.As most of the auto scaling algorithms have drawbacks such as latency,coarse-grained and high overhead,inspired by cache ideology,suspended virtual machines were introduced to speed the provision speed.An agile auto scaling algorithm was designed based on auto-regressive and moving average(ARMA)with two level prediction to achieve fine-grained resource allocation,and strategies such as quantile statistic,allocation of extra resources,delayed release of resources were adopted to further satisfy the quality of service(QoS).Experimental results show that auto scaling can further save cloud resources,while improving the service quality with the workload of NETEASE Cloud Reader.

关 键 词：云计算 弹性计算 负载预测 自回归滑动平均模型 二级预测 服务质量 

分 类 号：TM715[电气工程—电力系统及自动化]