信息中心网络中不确定性下具有移动性预测的主动缓存

Noor Abani, T. Braun, M. Gerla
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引用次数: 70

摘要

主动缓存是减少检索可预测内容请求的延迟、减轻回程流量和减轻由切换引起的延迟的关键因素。在移动网络中,主动缓存依赖于移动性预测来定位移动设备的下一个位置,从而确定必须预取内容的节点。先前提出的主动缓存策略采用边缘独占缓存和在多个边缘节点上缓存冗余副本来解决预测的不确定性。在本文中,我们提出了一种主动缓存策略,该策略利用ICN在网络中任何地方缓存数据的灵活性,而不仅仅是在边缘,就像传统的内容交付网络一样。本文的主要贡献是利用熵来衡量迁移预测的不确定性,并找到最佳预取节点,从而消除冗余。虽然在网络层次结构中较高的级别预取会比在边缘产生更高的延迟,但我们的评估结果表明,延迟的增加并不会抵消主动缓存的性能提升。此外,由于减少了服务器负载和实现了缓存冗余,这些收益被放大了。
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Proactive caching with mobility prediction under uncertainty in information-centric networks
Proactive caching can be a key enabler for reducing the latency of retrieving predictable content requests, alleviating backhaul traffic and mitigating latency caused by handovers. In mobile networks, proactive caching relies on mobility prediction to locate the mobile device's next location and hence the node that must prefetch the content. Previously proposed proactive caching strategies use edge caching exclusively and cache redundant copies on multiple edge nodes to address prediction uncertainty. In this paper, we present a proactive caching strategy that leverages ICN's flexibility of caching data anywhere in the network, rather than just at the edge, like conventional content delivery networks. The main contribution of the paper is to use entropy to measure mobility prediction uncertainty and locate the best prefetching node, thus eliminating redundancy. While prefetching at levels higher in the network hierarchy incurs higher delays than at the edge, our evaluation results show that the increase in latency does not negate the performance gains of proactive caching. Moreover, the gains are amplified by the reduction in server load and cache redundancy achieved.
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