An Efficient Framework for Peer Selection in Dynamic P2P Network Using Q Learning with Fuzzy Linear Programming

This paper proposes a new approach to integrating Q learning into the fuzzy linear programming (FLP) paradigm to improve peer selection in P2P networks. Using Q learning, the proposed method employs real-time feedback to adjust and update peer selection policies. The FLP framework enriches this process by dealing with imprecise information through fuzzy logic. It is used to achieve multiple objectives, such as enhancing the throughput rate, reducing the delay, and guaranteeing a reliable connection. This integration effectively solves the problem of network uncertainty, making the network configuration more stable and flexible. It is also important to note that throughout the use of the Q-learning agent in the network, various state metric indicators, including available bandwidth, latency, packet drop rates, and connectivity of nodes, are observed and recorded. It then selects actions by choosing optimal peers for each node and updating a Q table that defines states and actions based on these performance indices. This reward system guides the agent’s learning, refining its peer selection policy over time. The FLP framework supports the Q-learning agent by providing optimized solutions that balance conflicting objectives under uncertain conditions. Fuzzy parameters capture variability in network metrics, and the FLP model solves a fuzzy linear programming problem, offering guidelines for the Q-learning agent’s decisions. The proposed method is evaluated under different experimental settings to reveal its effectiveness. The Erdos–Renyi model simulation is used, and it shows that throughput increased by 21% and latency decreased by 40%. The computational efficiency was also notably improved, with computation times diminishing by up to five orders of magnitude compared to traditional methods.