A Load-Aware Replica Selection Strategy with Multi-Armed Bandits and Adaptive Redundancy in ICN

Information-Centric Networking (ICN) leverages content naming and pervasive innetwork caching to enable retrieval from multiple replicas, making replica selection a key determinant of performance. However, effective selection remains difficult, as replica nodes are constrained by limited capacity, bursty workloads, and dynamic path variations.

Traditional approaches based on historical measurements or prediction require frequent probing, which incurshigh overhead while still failing to explore new replicas effectively. To mitigate this, the Multi-Armed Bandit (MAB) framework has been adopted to balance exploration and exploitation. Yet, existing MAB-based methods still face high exploration costs in large-scale scenarios and lack explicit control of tail latency.