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Power and performance management of virtualized computing environments via lookahead control

机译:通过提前控制实现虚拟化计算环境的电源和性能管理

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There is growing incentive to reduce the power consumed by large-scale data centers that host online services such as banking, retail commerce, and gaming. Virtualization is a promising approach to consolidating multiple online services onto a smaller number of computing resources. A virtualized server environment allows computing resources to be shared among multiple performanceisolated platforms called virtual machines. By dynamically provisioning virtual machines, consolidating the workload,and turning servers on and off as needed, data center opera-tors can maintain the desired quality-of-service (QoS) while achieving higher server utilization and energy efficiency.We implement and validate a dynamic resource provisioning framework for virtualized server environments wherein the provisioning problem is posed as one of sequential optimization under uncertainty and solved using a lookahead control scheme. The proposed approach accounts for the switching costs incurred while provisioning virtual machines and explicitly encodes the corresponding risk in the optimization problem. Experiments using the Trade6 enterprise application show that a server cluster managed by the controller conserves, on average, 22% of the power required by a system without dynamic control while still maintaining QoS goals. Finally, we use trace-based simulations to analyze controller performance on server clusters larger than our testbed, and show how concepts from approximation theory can be used to further reduce the computational burden of controlling large systems.
机译:越来越多的动机要求减少托管在线服务(例如银行,零售商业和游戏)的大型数据中心的功耗。虚拟化是将多种在线服务整合到较少数量的计算资源上的一种有前途的方法。虚拟服务器环境允许计算资源在称为虚拟机的多个性能隔离平台之间共享。通过动态预配虚拟机,整合工作负载并根据需要打开和关闭服务器,数据中心运营商可以在保持更高服务器利用率和能源效率的同时,保持所需的服务质量(QoS)。用于虚拟服务器环境的动态资源供应框架,其中供应问题是不确定性下的顺序优化之一,并使用超前控制方案解决。所提出的方法解决了在配置虚拟机时产生的切换成本,并在优化问题中明确编码了相应的风险。使用Trade6企业应用程序进行的实验表明,由控制器管理的服务器集群平均可节省不具有动态控制的系统所需电源的22%,同时仍保持QoS目标。最后,我们使用基于跟踪的模拟来分析比测试床更大的服务器群集上的控制器性能,并说明如何使用近似理论的概念进一步减轻控制大型系统的计算负担。

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