Payment Channel Networks: Single-Hop Scheduling for Throughput Maximization

Abstract

Payment channel networks (PCNs) have emerged as a scalability solution for blockchains built on the concept of a payment channel: a setting that allows two parties to safely transact between themselves in high frequencies by updating pre-committed balances. Transaction requests in PCNs may be declined because of unavailability of funds due to temporary uneven distribution of the channel balances. In this paper, we investigate how to alleviate unnecessary payment blockage via proper prioritization of the transaction execution order. Specifically, we consider the scheduling problem in a payment channel: as transactions continuously arrive on both sides, nodes need to decide which ones to process and when, in order to maximize channel throughput. We introduce a stochastic model to capture the dynamics of a payment channel under discrete stochastic arrivals, with incoming transactions potentially held in buffers up until some deadline in order to enable more elaborate processing decisions. We describe a scheduling policy that maximizes the channel success rate/throughput, formally prove its optimality for fixed-amount transactions, and also show its superiority in the case of heterogeneous amounts via experiments in our discrete event simulator. Overall, our work is a step in the direction of formal research on improving PCN performance.