arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2006.11283cs.ITcs.NImath.IT
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无线异构网络中缓存的空间集中性

Spatial Concentration of Caching in Wireless Heterogeneous Networks

Derya Malak, Muriel Médard, Jeffrey G. Andrews

摘要:

我们提出了一个分散的高速缓存策略,使内容放置决策的基础上成对的高速缓存节点之间的相互作用的无线异构网络。我们称我们提出的方案γ-排除缓存放置(GEC),其中参数γ控制排除半径,阻止附近的缓存存储冗余内容。GEC考虑了项目的流行度和节点的缓存优先级,并利用负相关来放松经典的0-1背包问题,以产生空间上平衡的采样跨缓存。我们表明,GEC保证了更好的浓度(降低方差)所需的高速缓存存储的大小比最先进的,高速缓存大小的限制,可以满足高概率。给定一个缓存命中概率目标,我们比较了三种缓存方案所需的缓存大小的95%置信区间:(i)独立放置,(ii)硬排除缓存(HEC),和(iii)建议的GEC方法。对于均匀的空间流量,我们证明,GEC提供了约3倍和2倍的减少所需的缓存大小分别超过(i)和(ii)。对于基于城市场景中实际高峰时间变化的非均匀空间交通,收益甚至更大。

英文摘要:

We propose a decentralized caching policy for wireless heterogeneous networks that makes content placement decisions based on pairwise interactions between cache nodes. We call our proposed scheme γ-exclusion cache placement (GEC), where a parameter γ controls an exclusion radius that discourages nearby caches from storing redundant content. GEC takes into account item popularity and the nodes' caching priorities and leverages negative dependence to relax the classic 0-1 knapsack problem to yield spatially balanced sampling across caches. We show that GEC guarantees a better concentration (reduced variance) of the required cache storage size than the state of the art, and that the cache size constraints can be satisfied with high probability. Given a cache hit probability target, we compare the 95\% confidence intervals of the required cache sizes for three caching schemes: (i) independent placement, (ii) hard exclusion caching (HEC), and (iii) the proposed GEC approach. For uniform spatial traffic, we demonstrate that GEC provides approximately a 3x and 2x reduction in required cache size over (i) and (ii), respectively. For non-uniform spatial traffic based on realistic peak-hour variations in urban scenarios, the gains are even greater.

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