Neighboring-Aware Caching in Heterogeneous Edge Networks by Actor-Attention-Critic Learning

With the development of network technology and the surge in demand, the speed and throughput of data and applications are leading to the skyrocketing increase in traffic. The communication and collaboration between heterogeneous edge servers are indispensable. In this scenario with heterogeneous edges, there is a common understanding on the fact that an effective edge caching algorithm could play the role of enabler to reduce the network resource consumption and content fetch delay. However, most of the existing studies on multi-agent caching methods focus more on the overall situation, while ignoring the mutual influence between different agents. In this context, we model the edge caching content replacement problem as a Markov process and deploy attention mechanism based on the Actor-Attention-Critic algorithm to realize a neighboring-aware edge caching (NAEC) strategy. The proposed method makes full use of the communication between base stations to exchange neighboring information, so that we can reduce the pressure on the backbone and further improve user satisfaction. The simulation results have verified the feasibility and effectiveness of the proposed algorithm.