Design and Evaluation of the Optimal Cache Allocation for Content-Centric Networking

Design and Evaluation of the Optimal Cache Allocation for Content-Centric Networking
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以内容为中心的网络的最佳缓存分配的设计和评估

DOI:
10.1109/tc.2015.2409848
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发表时间:
2016
影响因子:
3.7
通讯作者:
Xie, Gaogang
Xie, Gaogang
中科院分区:
计算机科学2区
文献类型:
--
作者:
Li, Zhenyu;Tyson, Gareth;Uhlig, Steve;Xie, Gaogang

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以内容为中心的网络(CCN)是一个很有前途的框架,可以围绕内容的概念重建互联网的转发基础。CCN提倡无处不在的网络内缓存来增强内容交付,因此每个路由器都有存储空间来缓存频繁请求的内容。在这项工作中,我们专注于该高速缓存分配问题,即,如何分配该高速缓存容量下的总存储预算的网络路由器。我们首先将这个问题转化为一个内容放置问题,并通过两步方法获得最优解。然后,我们提出了一个次优的启发式方法的基础上节点的中心性,这是更实用的动态网络频繁的内容发布。我们调查通过模拟的因素,影响最佳的缓存分配,也许更重要的是,我们使用现实生活中的互联网拓扑结构和视频访问日志从一个大规模的互联网视频提供商来评估各种缓存分配方法的性能。我们观察到,网络拓扑结构和内容流行度是两个重要的因素,影响缓存容量到底应该放在哪里。此外,与最优分配相比,启发式方法仅具有非常有限的性能损失。最后,利用我们的研究结果,我们为网络运营商提供了关于路由器上CCN缓存容量的最佳部署的建议。
Content-centric networking (CCN) is a promising framework to rebuild the Internet's forwarding substrate around the concept of content. CCN advocates ubiquitous in-network caching to enhance content delivery, and thus each router has storage space to cache frequently requested content. In this work, we focus on the cache allocation problem, namely, how to distribute the cache capacity across routers under a constrained total storage budget for the network. We first formulate this problem as a content placement problem and obtain the optimal solution by a two-step method. We then propose a suboptimal heuristic method based on node centrality, which is more practical in dynamic networks with frequent content publishing. We investigate through simulations the factors that affect the optimal cache allocation, and perhaps more importantly we use a real-life Internet topology and video access logs from a large scale Internet video provider to evaluate the performance of various cache allocation methods. We observe that network topology and content popularity are two important factors that affect where exactly should cache capacity be placed. Further, the heuristic method comes with only a very limited performance penalty compared to the optimal allocation. Finally, using our findings, we provide recommendations for network operators on the best deployment of CCN caches capacity over routers.
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