Should my Blockchain Learn to Drive? A Study of Hyperledger Fabric

TL;DR

This paper investigates the potential for self-driving Hyperledger Fabric blockchains to autonomously reconfigure for optimal performance, demonstrating significant improvements in throughput and latency through experimental autonomous systems.

Contribution

It identifies key parameters for autonomous adaptation in Hyperledger Fabric and implements prototype systems to evaluate their effectiveness.

Findings

Up to 11% increase in success throughput

30% reduction in latency

Feasibility of autonomous reconfiguration demonstrated

Abstract

Similar to other transaction processing frameworks, blockchain systems need to be dynamically reconfigured to adapt to varying workloads and changes in network conditions. However, achieving optimal reconfiguration is particularly challenging due to the complexity of the blockchain stack, which has diverse configurable parameters. This paper explores the concept of self-driving blockchains, which have the potential to predict workload changes and reconfigure themselves for optimal performance without human intervention. We compare and contrast our discussions with existing research on databases and highlight aspects unique to blockchains. We identify specific parameters and components in Hyperledger Fabric, a popular permissioned blockchain system, that are suitable for autonomous adaptation and offer potential solutions for the challenges involved. Further, we implement three demonstrative locally autonomous systems, each targeting a different layer of the blockchain stack, and conduct experiments to understand the feasibility of our findings. Our experiments indicate up to 11% improvement in success throughput and a 30% decrease in latency, making this a significant step towards implementing a fully autonomous blockchain system in the future.