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Achieving High Throughput for Heterogeneous Networks With Consecutive Caching and Adaptive Retrieval
IEEE Transactions on Network Science and Engineering ( IF 6.7 ) Pub Date : 2020-07-21 , DOI: 10.1109/tnse.2020.3010939
Ruidong Li , Kazuhisa Matsuzono , Hitoshi Asaeda , Xiaoming Fu

Heterogeneous networks raise the challenge on ubiquitous connections among heterogeneous devices and networking protocols. As a promising approach to meet this challenge, Information-centric networking (ICN) offers a new communication paradigm which can conceal the heterogeneity of underlying networks. However, it suffers from the problem of segmented cached chunks, which results in low throughput caused by high frequency of switching among different nodes holding chunk copies, and the large Interest packet overhead (IPO). Although the studies on in-network caching or Interest pipelining based on delay estimation help to improve ICN performance, they cannot address such problem and further improve ICN performance fundamentally, because of their insufficient considerations on the interplay between caching and transport protocols. We conduct the experiments and observe that 1) data chunks are cached in a distributed manner with ICN, and 2) range-based data retrieval can reduce switch-over frequency and IPO. Based on these observations, we propose the adaptive retrieval with consecutive caching (ARCC) scheme, which is composed of the consecutive data chunk caching (ConCaching) and adaptive data chunk retrieval (ACUR). ARCC bridges the gap between caching and transport, where intermediate nodes on transmission path only cache the consecutive data chunks with the size above a threshold, while users can adjust the range of requested data chunks to maximize the throughput. This way, at most a range of consecutive data chunks can be retrieved by one Interest, and users retrieve data from the small set of nodes with low switch-over frequency. The intensive simulations show that the proposed mechanisms can achieve the objectives of consecutive chunk caching, in terms of substantial reduction in IPO and switch-over frequency and higher throughput compared with the existing pipeline mechanism in ICN.

中文翻译:


通过连续缓存和自适应检索实现异构网络的高吞吐量



异构网络对异构设备和网络协议之间的普遍连接提出了挑战。作为应对这一挑战的一种有前途的方法,以信息为中心的网络(ICN)提供了一种新的通信范式,可以隐藏底层网络的异构性。然而,它存在分段缓存块的问题,导致持有块副本的不同节点之间的频繁切换导致吞吐量低,并且兴趣包开销(IPO)较大。尽管基于延迟估计的网内缓存或兴趣管道的研究有助于提高ICN性能,但由于对缓存和传输协议之间的相互作用考虑不够,无法从根本上解决这一问题并进一步提高ICN性能。我们进行实验并观察到:1)数据块通过 ICN 以分布式方式缓存,2)基于范围的数据检索可以减少切换频率和 IPO。基于这些观察,我们提出了连续缓存自适应检索(ARCC)方案,该方案由连续数据块缓存(ConCaching)和自适应数据块检索(ACUR)组成。 ARCC弥合了缓存和传输之间的差距,其中传输路径上的中间节点仅缓存大小高于阈值的连续数据块,而用户可以调整请求的数据块的范围以最大化吞吐量。这样,一个Interest最多可以检索一系列连续的数据块,并且用户可以以较低的切换频率从一小组节点中检索数据。 密集的仿真表明,与ICN中现有的管道机制相比,所提出的机制可以实现连续块缓存的目标,大幅减少IPO和切换频率,提高吞吐量。
更新日期:2020-07-21
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