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Payment channel networks (PCNs) have emerged as a scalability solution forblockchains built on the concept of a payment channel: a setting that allowstwo nodes to safely transact between themselves in high frequencies based onpre-committed peer-to-peer balances. Transaction requests in these networks maybe declined because of unavailability of funds due to temporary unevendistribution of the channel balances. In this paper, we investigate how toalleviate unnecessary payment blockage via proper prioritization of thetransaction execution order. Specifically, we consider the scheduling problemin PCNs: as transactions continuously arrive on both sides of a channel, nodesneed to decide which ones to process and when in order to maximize theirobjective, which in our case is the channel throughput. We introduce astochastic model to capture the dynamics of a payment channel under randomarrivals, and propose that channels can hold incoming transactions in buffersup to some deadline in order to enable more elaborate processing decisions. Wedescribe a policy that maximizes the channel success rate/throughput foruniform transaction requests of fixed amounts, both in the presence and absenceof buffering capabilities, and formally prove its optimality. We also develop adiscrete event simulator of a payment channel, and evaluate different heuristicscheduling policies in the more general heterogeneous amounts case, with theresults showing superiority of the heuristic extension of our policy in thiscase as well. Our work opens the way for more formal research on improving PCNperformance via joint consideration of routing and scheduling decisions.

State-Dependent Processing in Payment Channel Networks for Throughput Optimization