Automated control of multiple virtualized resources
Automated control of multiple virtualized resources
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DOI：
10.1145/1519065.1519068
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发表时间：
2009-04
期刊：
影响因子：
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通讯作者：
Pradeep Padala;Kai-yuan Hou;K. Shin;Xiaoyun Zhu;Mustafa Uysal;Zhikui Wang;S. Singhal;A. Merchant-A.-Merch
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文献类型：
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作者：
Pradeep Padala;Kai-yuan Hou;K. Shin;Xiaoyun Zhu;Mustafa Uysal;Zhikui Wang;S. Singhal;A. Merchant-A.-Merch
虚拟化数据中心支持在托管的应用程序之间共享资源。然而，很难满足共享基础设施上应用程序的服务级别目标（slo），因为应用程序工作负载和资源消耗模式会随时间变化。在本文中，我们提出了一个自动适应动态工作负载变化的资源控制系统AutoControl，以实现应用程序的slo。自动控制是一个在线模型估计器和一种新型的多输入，多输出（MIMO）资源控制器的组合。模型估计器捕获应用程序性能和资源分配之间的复杂关系，而MIMO控制器分配适量的多个虚拟化资源以实现应用程序的slo。我们对RUBiS和TPC-W基准测试以及生产跟踪驱动的工作负载进行的实验评估表明，AutoControl可以通过相应地分配每个资源来检测和缓解CPU和磁盘I/O瓶颈，这些瓶颈随着时间的推移在多个节点上发生。我们还展示了AutoControl可以用于在资源争用期间根据应用程序优先级提供服务差异化。
Virtualized data centers enable sharing of resources among hosted applications. However, it is difficult to satisfy service-level objectives(SLOs) of applications on shared infrastructure, as application workloads and resource consumption patterns change over time. In this paper, we present AutoControl, a resource control system that automatically adapts to dynamic workload changes to achieve application SLOs. AutoControl is a combination of an online model estimator and a novel multi-input, multi-output (MIMO) resource controller. The model estimator captures the complex relationship between application performance and resource allocations, while the MIMO controller allocates the right amount of multiple virtualized resources to achieve application SLOs. Our experimental evaluation with RUBiS and TPC-W benchmarks along with production-trace-driven workloads indicates that AutoControl can detect and mitigate CPU and disk I/O bottlenecks that occur over time and across multiple nodes by allocating each resource accordingly. We also show that AutoControl can be used to provide service differentiation according to the application priorities during resource contention.