Design and Evaluation of Probabilistic Caching in Information-Centric Networking

Design and Evaluation of Probabilistic Caching in Information-Centric Networking
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以信息为中心的网络中概率缓存的设计和评估

DOI:
10.1109/access.2018.2841417
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发表时间:
2018-06
期刊:
影响因子:
3.9
通讯作者:
Lingling Li
Lingling Li
中科院分区:
计算机科学3区
文献类型:
--
作者:
Haibo Wu;Jun Li;Jiang Zhi;Yongmao Ren;Lingling Li

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网内缓存是以信息为中心的网络(ICN)的一个关键特性。然而,ICN缓存中仍然存在挑战,例如如何将内容副本放置在缓存节点之间以最大化缓存系统效益而不引入太多开销。在本文中,我们制定的内容放置问题,并提出了一个分布式的概率缓存策略,以提高缓存效率。每个节点单独进行缓存决策,以一定的概率缓存通过的内容,该概率与内容流行度和内容放置效益成正比。作为我们的计划的一部分,我们还提出了一个准确的方法来预测内容流行度的变化。此外,提出了一种基于全局流行度的缓存方案,作为性能评估的基准。我们进行了广泛的模拟ndnSIM的基础上,树,内部AS,AS间的拓扑结构和评估我们的计划。结果表明,它优于国家的最先进的计划在缓存命中率,访问延迟,缓存操作成本,和链路带宽节省。它可以实现显着的性能改进,即使在小缓存大小的情况下。特别地,缓存操作的减少可以达到两个数量级。我们还研究了各种替换策略对缓存性能的影响,并进行了开销分析。最后,我们给出了一个简化的实现我们的计划,并通过仿真验证。
In-network caching is a key feature of information-centric networking (ICN). However, challenges still exist in ICN caching such as how to place content replicas among cache nodes to maximize cache system benefits without introducing too much overhead. In this paper, we formulate the content placement problem and propose a distributed probabilistic caching strategy to enhance cache efficiency. Each node makes cache decision individually and caches passing content with certain probability, which is proportional to content popularity and content placement benefit. As a component of our scheme, we also propose an accurate method to predict the variations of content popularity. Besides, a global-popularity-based caching scheme is proposed to be used as a benchmark for performance evaluation. We conduct extensive simulations based on ndnSIM and evaluate our scheme on tree, intra-AS, and inter-AS topologies. Results indicate it outperforms the state-of-art schemes in terms of cache hit ratio, access latency, cache operation cost, and link bandwidth savings. It can achieve dramatic performance improvement, even in the case of a small cache size. In particular, the reduction of caching operation can reach up to two orders of magnitude. We also examine the impacts of various replacement policies on cache performance and perform overhead analysis. Finally, we give a simplified implementation of our scheme and validate it via simulations.
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