Popularity‐Aware In‐Network Caching for Edge Named Data Network

Abstract

The traditional centralized network architecture can lead to a bandwidth bottleneck in the core network. In contrast, in the information-centric network, decentralized in-network caching can alleviate the traffic flow pressure from the network center to the edge. In this paper, a popularity-aware in-network caching policy, namely, Pop, is proposed to achieve an optimal caching of network contents in the resource-constrained edge networks. Specifically, Pop senses content popularity and distributes content caching without adding additional hardware and traffic overhead. We conduct extensive performance evaluation experiments by using ndnSIM. The experiments showed that the Pop policy achieves 54.39% cloud service hit reduction ratio and 22.76% user request average hop reduction ratio and outperforms other policies including Leave Copy Everywhere, Leave Copy Down, Probabilistic Caching, and Random choice caching. In addition, we proposed an ideal caching policy (Ideal) as a baseline whose popularity is known in advance; the gap of Pop and Ideal in cloud service hit reduction ratio is 4.36%, and the gap in user request average hop reduction ratio is only 1.47%. More simulation results further show the accuracy of Pop in perceiving popularity of contents, and Pop has good robustness in different request scenarios.

An NDN Cache-Optimization Strategy Based on Dynamic Popularity and Replacement Value

“…Gui [34] proposed a cache-placement strategy based on compound popularity (content popularity and node popularity); this scheme aims to enhance the reuse rate of data packets by jointly considering the content popularity and the node popularity over a period of time. Yin [35] proposed a popularity-aware in-network caching policy (Pop), which can perceive the popularity of content without additional hardware and traffic overhead and realize the optimal caching of network content in edge networks with limited resources. Rashid [36] proposed coupling parameters to optimize content placement (COCP).…”