EchoFlow: A Workload-Aware Parameter Tuning Method for Blockchain Systems
2026-06-22 • Distributed, Parallel, and Cluster Computing
Distributed, Parallel, and Cluster Computing
AI summaryⓘ
The authors present EchoFlow, a system that automatically adjusts blockchain settings to improve performance depending on the workload. Instead of using a single fixed setup, EchoFlow learns the best configurations using a combination of reinforcement learning and genetic algorithms, which helps it learn faster. Their tests show that EchoFlow works better than other methods and saves time in training. This makes blockchain systems run more efficiently under different conditions.
blockchainparameter tuningreinforcement learninggenetic algorithmworkload adaptationperformance optimizationdistributed learningsample generation
Authors
Ben Lian, Linpeng Jia, Xing Chen, Xiaofeng Chen, Yi Sun
Abstract
Blockchain systems expose a large number of tunable parameters that significantly influence system performance. However, in practice, a single parameter configuration is often applied across different workloads, leaving substantial unexploited performance potential. To address this, we propose EchoFlow, a blockchain parameter tuning framework that adaptively adjusts parameter configurations based on workload characteristics, enabling continuous performance optimization. EchoFlow employs a distributed reinforcement learning approach in which multiple actors perform parallel sampling to mitigate the substantial time required for sample generation in blockchain environments. To further accelerate convergence, we introduce a genetic algorithm during the initial phase of training to generate high-quality samples. Extensive experimental evaluations demonstrate that EchoFlow consistently outperforms existing methods across diverse workload scenarios while also reducing training time, highlighting its effectiveness and practical value.