基于web的Internet数据缓存进化模型

A. Vakali
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引用次数: 15

摘要

高速缓存是解决由于互联网上信息流通迅速增加而导致的带宽不足问题的标准方法。缓存一致性机制是影响缓存可用性和可靠性的关键组成部分。本文提出了一种利用遗传算法优化互联网缓存内容的模型,并通过跟踪驱动实验对该模型进行了检验。缓存的数据被认为是一个在模拟时间内由许多连续的缓存“代”进化的种群。通过使用Squid代理缓存服务器提供的跟踪来测试该模型。使用跟踪驱动的缓存,我们证明了所提出的进化机制改善了缓存的非过时性和一致性,并导致缓存内容的更新。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Web-based evolutionary model for Internet data caching
Caching is a standard solution to the problem of insufficient bandwidth caused by the rapid increase of information circulation across the Internet. Cache consistency mechanisms are a crucial component of each cache scheme influencing the cache usefulness and reliability. The paper presents a model for optimizing Internet cache content by the use of a genetic algorithm and examines the model by trace-driven experiments. Cached data are considered as a population evolving over simulated time by a number of successive cache "generations". The model is tested by the use of traces provided by a Squid proxy cache server. Using trace-driven caching, we show that the proposed evolutionary mechanisms improve cache nonstaleness and consistency and result in an updated cache content.
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