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Generalization of LRU Cache Replacement Policy with Applications to Video Streaming

机译:LRU缓存替换策略及其在视频流应用中的一般化

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Caching plays a crucial role in networking systems to reduce the load on the network and is commonly employed by content delivery networks (CDNs) to improve performance. One of the commonly used mechanisms, Least Recently Used (LRU), works well for identical file sizes. However, for asymmetric file sizes, the performance deteriorates. This article proposes an adaptation to the LRU strategy, called gLRU, where the file is sub-divided into equal-sized chunks. In this strategy, a chunk of the newly requested file is added in the cache, and a chunk of the least-recently-used file is removed from the cache. Even though approximate analysis for the hit rate has been studied for LRU, the analysis does not extend to gLRU, since the metric of interest is no longer the hit rate as the cache has partial files. This article provides a novel approximation analysis for this policy where the cache may have partial file contents. The approximation approach is validated by simulations. Further, gLRU outperforms the LRU strategy for a Zipf file popularity distribution and censored Pareto file size distribution for the file download times. Video streaming applications can further use the partial cache contents to help the stall duration significantly, and the numerical results indicate significant improvements (32%) in stall duration using the gLRU strategy as compared to the LRU strategy. Furthermore, the gLRU replacement policy compares favorably to two other cache replacement policies when simulated on MSR Cambridge Traces obtained from the SNIA IOTTA repository.
机译:缓存在减少网络负载的网络系统中起着至关重要的作用,内容分发网络(CDN)通常使用缓存来提高性能。最常用的机制之一,最近最少使用(LRU),对于相同的文件大小,效果很好。但是,对于不对称的文件大小,性能会下降。本文提出了一种称为gLRU的LRU策略的改编方案,其中将文件细分为相等大小的块。在此策略中,将新请求的文件块添加到缓存中,并将最近使用最少的文件块从缓存中删除。即使已针对LRU研究了命中率的近似分析,但由于高速缓存具有部分文件,因此关注的指标不再是命中率,因此该分析不会扩展到gLRU。本文针对此策略提供了一种新颖的近似分析,其中缓存可能具有部分文件内容。通过仿真验证了近似方法。此外,对于文件下载时间,gLRU的表现优于Zipf文件受欢迎程度分布的LRU策略和审查的Pareto文件大小分布。视频流应用程序可以进一步使用部分缓存内容来显着地帮助延长停顿持续时间,并且数值结果表明,与LRU策略相比,使用gLRU策略可以显着改善停顿持续时间(32%)。此外,在从SNIA IOTTA存储库获得的MSR Cambridge Traces上进行模拟时,gLRU替换策略优于其他两个缓存替换策略。

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