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High-Performance Resource Allocation and Request Redirection Algorithms for Web Clusters

机译：Web集群的高性能资源分配和请求重定向算法

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摘要

With increasing richness in features such as personalization of content, web applications are becoming increasingly complex and hence compute intensive. Traditional approaches for improving performance of static content web sites have been based on the assumption that static content such as images are network intensive. However, these methods are not applicable to the dynamic content applications which are more compute intensive than static content. This paper proposes a suite of algorithms which jointly optimize the performance of dynamic content applications by reducing the client access times while also minimizing the resource utilization. A server migration algorithm allocates servers on-demand within a cluster such that the client access times are not affected even under sudden overload conditions. Further, a server selection mechanism enables statistical multiplexing of resources across clusters by redirecting requests away from overloaded clusters. We also propose a cluster decision algorithm which decides whether to migrate in additional servers at the local cluster or redirect requests remotely under different workload conditions. Through a combination of analytical modeling, trace-driven simulation over traces from large e-commerce sites and testbed implementation, we explore the performance savings achieved by the proposed algorithms.
机译：随着内容个性化等功能的日益丰富，Web应用程序变得越来越复杂，因此计算量很大。用于提高静态内容网站性能的传统方法已基于以下假设：静态内容（例如图像）是网络密集型的。但是，这些方法不适用于动态内容应用程序，该应用程序比静态内容的计算量更大。本文提出了一套算法，可通过减少客户端访问时间并最小化资源利用率来共同优化动态内容应用程序的性能。服务器迁移算法按需分配群集中的服务器，这样即使在突然的过载情况下，客户端访问时间也不会受到影响。此外，服务器选择机制可以通过将请求重定向到过载的群集之外，从而实现群集之间资源的统计复用。我们还提出了一种群集决策算法，该算法可以决定是在本地群集中的其他服务器中迁移还是在不同的工作负载条件下远程重定向请求。通过分析模型的组合，对大型电子商务站点的跟踪进行跟踪驱动的模拟以及测试平台的实现，我们探索了所提出算法所实现的性能节省。

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