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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >Online Web Cluster Capacity Estimation and Its Application to Energy Conservation
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Online Web Cluster Capacity Estimation and Its Application to Energy Conservation

机译：在线Web集群容量估计及其在节能中的应用。

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摘要

Designers of data centers and Web servers aim to make on-demand allocation of resources to clients in order to lower the deployment cost of hosted services. Moreover, they must also minimize operating costs, such as energy consumption, by matching service-capacity demand with resource supply. However, since the term "capacity驴 is typically defined vaguely or inadequately, it is difficult to assess resource needs and, hence, servers, which are several times larger than needed at runtime, are usually deployed. The time-varying nature of the workload model further complicates the problem and necessitates an online capacity-estimation solution. To address this overprovisioning problem, we first define the capacity of a server cluster as the sustainable throughput subject to a request retransmission ratio constraint and then analyze different approaches to capacity estimation in a running system. Various capacity-estimation mechanisms, such as offline benchmarking and CPU-utilization evaluation, are discussed and compared with our queue-monitoring method. We employ several different data-collection methods (application instrumentation, user-space tools, Simple Network Management Protocol (SNMP), and kernel modules) to compare their effects on estimation accuracy. Of these, queue monitoring is found to provide a good and stable estimate of server capacity. To validate this finding, we propose a simple cluster-resizing mechanism and evaluate the energy-conservation performance. A good combination of data collection and online capacity estimation is found to make significantly more energy savings than traditional approaches (that is, static estimation and scheduled capacity). Our experimental results show that more than 40 percent of energy can be saved for regular daily usage patterns without any prior knowledge of the workload and that long start-up and shutdown delays affect energy savings considerably.
机译：数据中心和Web服务器的设计者旨在按需分配资源给客户端，以降低托管服务的部署成本。此外，它们还必须通过使服务能力需求与资源供应相匹配来最大程度地降低运营成本，例如能源消耗。但是，由于术语“容量驴”通常含糊不清或定义不充分，因此难以评估资源需求，因此通常会部署比运行时所需资源大几倍的服务器。工作负载的时变性质为进一步解决此问题，我们首先将服务器群集的容量定义为受请求重传比率约束的可持续吞吐量，然后分析不同的方法进行容量估计。讨论了各种容量估计机制，例如脱机基准测试和CPU使用率评估，并与我们的队列监视方法进行了比较。我们采用了几种不同的数据收集方法（应用程序工具，用户空间工具，简单网络管理）协议（SNMP）和内核模块），以比较它们对估计精度的影响。发现监视可以提供对服务器容量的良好且稳定的估计。为了验证这一发现，我们提出了一种简单的群集大小调整机制并评估了节能性能。与传统方法（即静态估计和计划容量）相比，数据收集和在线容量估计的良好结合可显着节省更多能源。我们的实验结果表明，对于常规的日常使用模式，无需事先了解工作量即可节省40％的能源，而且长时间的启动和关闭延迟会极大地影响节能效果。

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