Towards a predictive cache replacement strategy for multimedia content

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• 作者：Jeroen Famaey ; ^{jeroen.famaey@intec.ugent.be} ; Fré ; dé ; ric Iterbeke ; Tim Wauters ^{tim.wauters@intec.ugent.be} ; Filip De Turck ^{filip.deturck@intec.ugent.be}
• 关键词：Multimedia content delivery ; Caching strategies ; Popularity prediction
• 刊名：Journal of Network and Computer Applications
• 出版年：2013
• 出版时间：January, 2013
• 年：2013
• 卷：36
• 期：1
• 页码：219-227
• 全文大小：377 K

文摘

In recent years, telecom operators have been moving away from traditional broadcast-driven television, towards IP-based interactive and on-demand multimedia services. Consequently, multicast is no longer sufficient to limit the amount of generated traffic in the network. In order to prevent an explosive growth in traffic, caches can be strategically placed throughout the content delivery infrastructure. As the size of caches is usually limited to only a small fraction of the total size of all content items, it is important to accurately predict future content popularity. Traditional caching strategies only take into account the past when deciding what content to cache. Recently, a trend towards novel strategies that actually try to predict future content popularity has arisen. In this paper, we ascertain the viability of using popularity prediction in realistic multimedia content caching scenarios. The proposed generic popularity prediction algorithm is capable of predicting future content popularity, independent of specific content and service characteristics. Additionally, a novel cache replacement strategy, which employs the popularity prediction algorithm when making its decisions, is introduced. A detailed evaluation, based on simulation results using trace files from an actual deployed Video on Demand service, was performed. The evaluation results are used to determine the merits of popularity-based caching compared to traditional strategies. Additionally, the synergy between several parameters, such as cache size and prediction window, is investigated. Results show that the proposed prediction-based caching strategy has the potential to significantly outperform state-of-the-art traditional strategies. Specifically, the evaluated Video on Demand scenario showed a performance increase of up to 20 % in terms of cache hit rate.