Graph Learning BFT: A Design of Consensus System for Distributed Ledgers

Abstract

Distributed ledger technology faces scalability problems due to a long commit time despite recent successes for cryptocurrency. Small group consensus studies have improved this scalability of distributed ledgers. However, they still have problems of the consensus process itself. For example, most blockchain systems perform serialized block proposal and consensus processing, guarantee the finality with high overhead, and handle byzantine nodes inefficiently. To address these problems, we propose a consensus system, named graph learning byzantine fault tolerance (GL BFT), which offers high parallelism and low latency under Byzantine fault. To do this, we enable a parallel pipelined agreement by separating the block proposal and the consensus process. Second, we devise two techniques of merging blocks and commit learning to guarantee the finality with little overhead. Finally, we present a path learning approach which chooses optimal paths to handle Byzantine fault. The proposed GL BFT can achieve instant finality with low message overhead among a small group of nodes even if Byzantine nodes exit. Also, we evaluate its performance on an open source blockchain protocol. Experimental results show that our design reduces data traffic required by the consensus up to 30%, one transaction is finalized within a few seconds, and optimal performance is maintained.