AdaChain: A Learned Adaptive Blockchain

TL;DR

AdaChain is a reinforcement learning-based framework that dynamically adapts blockchain architectures at runtime to optimize throughput under changing workloads, outperforming fixed setups with minimal overhead.

Contribution

It introduces a novel adaptive blockchain system that uses reinforcement learning to select optimal architectures dynamically, addressing workload variability in BaaS environments.

Findings

AdaChain quickly converges to optimal architectures.

It significantly improves transaction throughput.

It incurs low additional overhead.

Abstract

This paper presents AdaChain, a learning-based blockchain framework that adaptively chooses the best permissioned blockchain architecture in order to optimize effective throughput for dynamic transaction workloads. AdaChain addresses the challenge in the Blockchain-as-a-Service (BaaS) environments, where a large variety of possible smart contracts are deployed with different workload characteristics. AdaChain supports automatically adapting to an underlying, dynamically changing workload through the use of reinforcement learning. When a promising architecture is identified, AdaChain switches from the current architecture to the promising one at runtime in a way that respects correctness and security concerns. Experimentally, we show that AdaChain can converge quickly to optimal architectures under changing workloads, significantly outperform fixed architectures in terms of the number of successfully committed transactions, all while incurring low additional overhead.