Imbalance measure and proactive channel rebalancing algorithm for the Lightning Network

TL;DR

This paper introduces a new imbalance measure and a greedy rebalancing algorithm for the Lightning Network, significantly improving payment success rates and network balance through local, proactive adjustments.

Contribution

It proposes a novel imbalance metric and a greedy heuristic for local rebalancing, enhancing network stability and payment success in the Lightning Network.

Findings

Imbalance distribution improves with the heuristic, KS distance of 0.74.

Payment success rate increases from 11.2% to 98.3%.

Median payment size increases from 0 to 0.5 mBTC.

Abstract

Making a payment in a privacy-aware payment channel network is achieved by trying several payment paths until one succeeds. With a large network, such as the Lightning Network, a completion of a single payment can take up to several minutes. We introduce a network imbalance measure and formulate the optimization problem of improving the balance of the network as a sequence of rebalancing operations of the funds within the channels along circular paths within the network. As the funds and balances of channels are not globally known, we introduce a greedy heuristic with which every node despite the uncertainty can improve its own local balance. In an empirical simulation on a recent snapshot of the Lightning Network we demonstrate that the imbalance distribution of the network has a Kolmogorov-Smirnoff distance of 0.74 in comparison to the imbalance distribution after the heuristic is applied. We further show that the success rate of a single unit payment increases from 11.2% on the imbalanced network to 98.3% in the balanced network. Similarly, the median possible payment size across all pairs of participants increases from 0 to 0.5 mBTC for initial routing attempts on the cheapest possible path. We provide an empirical evidence that routing fees should be dropped for proactive rebalancing operations. Executing 4 different strategies for selecting rebalancing cycles lead to similar results indicating that a collaborative approach within the friend of a friend network might be preferable from a practical point of view