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Unravelling the Impact of Temporal and Geographical Locality in Content Caching Systems

机译:揭示内容缓存系统中时间和地理位置的影响

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摘要

To assess the performance of caching systems, the definition of a proper process describing the content requests generated by users is required. Starting from the analysis of traces of YouTube video requests collected inside operational networks, we identify the characteristics of real traffic that need to be represented and those that instead can be safely neglected. Based on our observations, we introduce a simple, parsimonious traffic model, named shot noise model (SNM), that allows us to capture temporal and geographical locality of content popularity. The SNM is sufficiently simple to be effectively employed in both analytical and scalable simulative studies of caching systems. We demonstrate this by analytically characterizing the performance of the LRU caching policy under the SNM, for both a single cache and a network of caches. With respect to the standard independent reference model (IRM), some paradigmatic shifts, concerning the impact of various traffic characteristics on cache performance, clearly emerge from our results.
机译:为了评估缓存系统的性能,需要定义适当的过程来描述用户生成的内容请求。从分析在运营网络内部收集的YouTube视频请求的痕迹开始,我们确定需要表示的真实流量的特征以及可以安全忽略的真实流量的特征。根据我们的观察,我们引入了一个简单的简约流量模型,称为散粒噪声模型(SNM),它使我们能够捕获内容流行度的时间和地理位置。 SNM非常简单,可以有效地用于缓存系统的分析和可扩展模拟研究中。我们通过分析表征单个缓存和缓存网络在SNM下LRU缓存策略的性能来证明这一点。关于标准独立参考模型(IRM),我们的结果清楚地出现了一些范式转变,涉及各种流量特性对缓存性能的影响。

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