LoCHiP: A Distributed Collaborative Cache Management Scheme at the Network Edge

Abstract

Using local caches is becoming a necessity to alleviate bandwidth pressure on cellular links, and a number of caching approaches advocate caching popular content at nodes with high centrality, which quantifies how well connected nodes are. These approaches have been shown to outperform caching policies unrelated to node connectivity. However, caching content at highly connected nodes places poorly connected nodes with low centrality at a disadvantage: in addition to their poor connectivity, popular content is placed far from them at the more central nodes. We propose reversing the way in which node connectivity is used for the placement of content in caching networks, and introduce a Low-Centrality High-Popularity (LoCHiP) caching algorithm that populates poorly connected nodes with popular content. We conduct a thorough evaluation of LoCHiP against other centrality-based caching policies and traditional caching methods using hit rate, and hop-count to content as performance metrics. The results show that LoCHiP outperforms significantly the other methods.

Adaptive In-Network Collaborative Caching for Enhanced Ensemble Deep Learning at Edge

“…Tang et al [31] proposed caching mechanisms in collaborative edge-cloud computing architecture, which can implement the caching paradigm in cloud for frequent n-hop neighbor activity regions. Khan et al [32] proposed reversing the way in which node connectivity is used for the placement of content in caching networks and introduced a Low-Centrality High-Popularity (LoCHiP) caching algorithm that populates poorly connected nodes with popular content. Wei et al [33] presented a system that automatically parallelizes serial imperative ML programs on distributed caching.…”