Adaptive Requesting in Decentralized Edge Networks via Non-Stationary Bandits
This addresses the challenge of efficient content delivery in edge networks for applications requiring low latency, though it appears incremental as it adapts existing bandit methods to a specific setting.
The paper tackles the problem of optimizing information freshness for time-sensitive clients in decentralized edge networks by formulating it as a non-stationary multi-armed bandit, and proposes an algorithm that achieves near-optimal performance with theoretical guarantees.
We study a decentralized collaborative requesting problem that aims to optimize the information freshness of time-sensitive clients in edge networks consisting of multiple clients, access nodes (ANs), and servers. Clients request content through ANs acting as gateways, without observing AN states or the actions of other clients. We define the reward as the age of information reduction resulting from a client's selection of an AN, and formulate the problem as a non-stationary multi-armed bandit. In this decentralized and partially observable setting, the resulting reward process is history-dependent and coupled across clients, and exhibits both abrupt and gradual changes in expected rewards, rendering classical bandit-based approaches ineffective. To address these challenges, we propose the AGING BANDIT WITH ADAPTIVE RESET algorithm, which combines adaptive windowing with periodic monitoring to track evolving reward distributions. We establish theoretical performance guarantees showing that the proposed algorithm achieves near-optimal performance, and we validate the theoretical results through simulations.