Transaction Workload-Aware Rebalancing Optimization Scheme for Payment Channel Networks

The Payment Channel Network (PCN) is an effective Layer-2 solution for addressing the scalability issues of blockchain-based cryptocurrencies. However, in practice, payment channels often suffer from balance depletion on one side, preventing further transactions from being executed through that channel. Rebalancing methods can alleviate this issue by actively shifting available balances across different payment channels to replenish depleted ones at minimal cost. However, existing rebalancing methods often determine the target balance of each channel subjectively, without ensuring that the rebalancing operation effectively increases the total value of executable transactions or maximizes balance-shifting efficiency within the PCN. To address these limitations, this paper establishes rebalancing evaluation models to quantify the impact of individual rebalancing operations on the total executable transaction value within the PCN. Based on these models, we design a hybrid rebalancing method that adaptively adjusts the rebalancing operation type, target balance, and participant selection according to the available balance and transaction workload of each payment channel, thereby maximizing the overall benefit of rebalancing operations on the total executed transaction value of the PCN. Experimental results demonstrate that our method improves the transaction success rate by up to 34.7% and reduces the average transaction latency by up to 4.4% compared with existing methods.