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Performance analysis of a client-side caching/prefetching system for Web traffic

机译:Web流量客户端缓存/预取系统的性能分析

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Network congestion remains one of the main barriers to the continuing success of the Internet. For Web users, congestion manifests itself in unacceptably long response times. One possible remedy to the latency problem is to use caching at the client, at the proxy server, or within the Internet. However, Web documents are becoming increasingly dynamic (i.e., have short lifetimes), which limits the potential benefit of caching. The performance of a Web caching system can be dramatically increased by integrating document prefetching (a.k.a. "proactive caching") into its design. Although prefetching reduces the response time of a requested document, it also increases the network load, as some documents will be unnecessarily prefetched (due to the imprecision in the prediction algorithm). In this study, we analyze the confluence of the two effects through a tractable mathematical model that enables us to establish the conditions under which prefetching reduces the average response time of a requested document. The model accommodates both passive client and proxy caching along with prefetching. Our analysis is used to dynamically compute the "optimal" number of documents to prefetch in the subsequent client's idle (think) period. In general, this optimal number is determined through a simple numerical procedure. Closed-form expressions for this optimal number are obtained for special yet important cases. We discuss how our analytical results can be used to optimally adapt the parameters of an actual prefetching system. Simulations are used to validate our analysis and study the interactions among various system parameters.
机译:网络拥塞仍然是互联网持续成功的主要障碍之一。对于Web用户,拥塞表现为无法接受的漫长响应时间。解决延迟问题的一种可能方法是在客户端,代理服务器或Internet上使用缓存。但是,Web文档变得越来越动态(即,具有较短的生存期),这限制了缓存的潜在好处。通过将文档预取(也称为“主动缓存”)集成到其设计中,可以大大提高Web缓存系统的性能。尽管预取会减少所请求文档的响应时间,但也会增加网络负载,因为某些文档会被不必要地预取(由于预测算法的不精确性)。在这项研究中,我们通过一个易于处理的数学模型来分析这两种效果的合流,该数学模型使我们能够建立条件,在该条件下预取可以减少所请求文档的平均响应时间。该模型可容纳被动客户端和代理缓存以及预取。我们的分析用于动态计算要在后续客户端的空闲(思考)期间预取的“最佳”文档数。通常,此最佳数量是通过简单的数值程序确定的。对于特殊但重要的情况,将获得此最佳数量的闭式表达式。我们讨论了如何将分析结果用于最佳地调整实际预取系统的参数。仿真用于验证我们的分析并研究各种系统参数之间的相互作用。

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