A Content-based Centrality Metric for Collaborative Caching in Information-Centric Fogs
Junaid Ahmed Khan, Cedric Westphal, and Yacine Ghamri-Doudane

TL;DR
This paper introduces a content-based centrality metric for fog networks that prioritizes nodes based on content delivery relevance rather than connectivity, leading to significantly improved caching performance.
Contribution
The paper proposes a novel content-based centrality metric for fog networks and demonstrates its effectiveness in enhancing collaborative caching performance.
Findings
CBC outperforms traditional centrality-based caching schemes
Achieves approximately 3x higher cache hit rate
Effective in large-scale realistic network topologies
Abstract
Information-Centric Fog Computing enables a multitude of nodes near the end-users to provide storage, communication, and computing, rather than in the cloud. In a fog network, nodes connect with each other directly to get content locally whenever possible. As the topology of the network directly influences the nodes' connectivity, there has been some work to compute the graph centrality of each node within that network topology. The centrality is then used to distinguish nodes in the fog network, or to prioritize some nodes over others to participate in the caching fog. We argue that, for an Information-Centric Fog Computing approach, graph centrality is not an appropriate metric. Indeed, a node with low connectivity that caches a lot of content may provide a very valuable role in the network. To capture this, we introduce acontent-based centrality (CBC) metric which takes into…
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