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On the performance analysis of distributed caching systems using a customizable Markov chain model

机译:基于可定制的马尔可夫链模型的分布式缓存系统性能分析

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In the last few years, Networks Operators (NO) have experienced an increased number of requests for video contents and rich media services, which are becoming increasingly popular. In view of the network scaling limitations, operators are developing their own caching systems to speed up the network performance. Indeed, disseminating caches in the infrastructure not only helps in absorbing the network's congestion, but in addition, brings content closer to users, which allows a reduced latency. Several studies have focused on improving the performance of such caching systems, especially in the context of Content-Centric Networking (CCN). In this paper, we propose a fairly generic model of caching systems that can be adapted very easily to represent different caching strategies, even the most advanced ones. Indeed, the proposed model of a single cache, named MACS, which stands for Markov chain-based Approximation of CCN Caching Systems, can be extended to represent an interconnection of caches under different schemes. In order to demonstrate the accuracy of our model, we proposed to derive the two most effective techniques in the literature, namely LCD and LRU-K, which may adapt to changing patterns of access. Simulation results using a discrete event simulator clearly show the accuracy of the proposed model under different network configurations.
机译:在过去的几年中,网络运营商(NO)经历了对视频内容和富媒体服务的越来越多的请求,这些请求越来越受欢迎。考虑到网络扩展的局限性,运营商正在开发自己的缓存系统以加快网络性能。实际上,在基础架构中分发缓存不仅有助于吸收网络的拥塞,而且还使内容更接近用户,从而减少了延迟。一些研究集中在提高此类缓存系统的性能上,特别是在以内容为中心的网络(CCN)的背景下。在本文中,我们提出了一个相当通用的缓存系统模型,该模型可以很容易地适应各种不同的缓存策略,甚至是最先进的策略。实际上,可以扩展名为MACS的单个缓存的建议模型,该模型代表基于Markov链的CCN缓存系统近似,可以表示不同方案下的缓存互连。为了证明我们模型的准确性,我们建议导出文献中最有效的两种技术,即LCD和LRU-K,它们可以适应不断变化的访问模式。使用离散事件模拟器的仿真结果清楚地表明了在不同网络配置下所提出模型的准确性。

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