Auto-tune: An efficient autonomous multi-path payment routing algorithm for Payment Channel Networks

Abstract

Payment Channel Network (PCN) is a scaling solution for Cryptocurrency networks. We advance the PCN multi-path routing by better modeling the system and incorporating the cost of routing fee and the privacy requirement of the channel balance. We design an autonomous routing algorithm, Auto-Tune, optimizing the routing concerning the success rate and the routing fee and utilizing the limited channel capacity information. The simulation result shows a significant performance gain in the success rate of Auto-Tune over the current PCN implementation based on single-path routing. To analyze the performance cost from the system requirement of the channel balance privacy, we compare Auto-Tune against the state-of-the-art Flash algorithm assuming the availability of the channel-balance information (such channel-balance information violates the PCN privacy requirement and does not comply with the current PCN implementations and practices). The simulation results show that the success rate and fee obtained by Auto-Tune are close to that obtained by Flash (which achieves the optimal fee result by using the exact channel-balance information).