PRESS: PRedictive Elastic ReSource Scaling for cloud systems
PRESS: PRedictive Elastic ReSource Scaling for cloud systems
复制标题
DOI：
10.1109/cnsm.2010.5691343
复制
发表时间：
2010-10
期刊：
影响因子：
--
通讯作者：
Zhenhuan Gong;Xiaohui Gu;J. Wilkes
中科院分区：
文献类型：
--
作者：
Zhenhuan Gong;Xiaohui Gu;J. Wilkes
云系统需要灵活的资源分配，以最大限度地减少资源调配成本，同时满足服务级别目标(SLO)。提出了一种新的云系统预测弹性资源伸缩(PRESS)方案。Press不引人注意地提取应用程序资源需求中的细粒度动态模式，并自动调整其资源分配。我们的方法利用轻量级信号处理和统计学习算法来实现动态应用程序资源需求的在线预测。我们已经在Xen上实现了新闻系统，并使用Rubis和来自Google的应用程序加载跟踪对其进行了测试。实验表明，该算法具有较好的资源预测精度，高估误差小于5%，低估误差接近于零，弹性资源伸缩可以显著减少资源浪费和SLO违规。
Cloud systems require elastic resource allocation to minimize resource provisioning costs while meeting service level objectives (SLOs). In this paper, we present a novel PRedictive Elastic reSource Scaling (PRESS) scheme for cloud systems. PRESS unobtrusively extracts fine-grained dynamic patterns in application resource demands and adjust their resource allocations automatically. Our approach leverages light-weight signal processing and statistical learning algorithms to achieve online predictions of dynamic application resource requirements. We have implemented the PRESS system on Xen and tested it using RUBiS and an application load trace from Google. Our experiments show that we can achieve good resource prediction accuracy with less than 5% over-estimation error and near zero under-estimation error, and elastic resource scaling can both significantly reduce resource waste and SLO violations.