Heuristic Approaches for Cache Node Placement in Content-Centric Networking Under Maximum Link Traffic Minimization

Abstract

This paper proposes heuristic approaches for cache node placement in Content-Centric Networking (CCN) with the objective of minimizing maximum link traffic. The work builds on an exact Integer Linear Programming (ILP) formulation from our earlier study, which jointly models routing and caching decisions but becomes computationally expensive for large-scale networks. To address this limitation, we develop scalable heuristic algorithms that approximate the ILP solution while requiring much lower computation time. We introduce four heuristic approaches, comprising two Linear Programming (LP)-based local search algorithms, 2Swap and GreedySwap, which exploit the fractional solution of the LP relaxation, and two proxy-based heuristics, Population-Weighted Closeness (PWC) and Population-Weighted Betweenness (PWB), which estimate cache utility using population-weighted centrality measures without solving any optimization model. Experimental evaluations on four network topologies show that the LP-based heuristics remain within 0-6% of the ILP optimum while reducing computation time by factors ranging from about 40× to more than 160×. Compared with the proxy-based heuristics, the LP-based heuristics consistently yield lower maximum link traffic across all settings. These results indicate that the proposed heuristic approaches provide effective and scalable solutions for cache node placement in practical CCN deployments.