HydraChain: A Cooperative MAPPO Architecture for Load Balancing in IoT Sharding Blockchain

Abstract

Sharding has become a significant approach to enhance blockchain scalability. However, existing sharding techniques applied in IoT scenarios suffer from transaction congestion due to imbalanced distribution of transactions across shards, which hinders intra-shard transaction processing. To overcome the above problems, this article proposes HydraChain in IoT scenarios, the first multiagent reinforcement learning based sharding blockchain system with account graph, for a throughput improvement of shards under real-time load balancing. Agents collaborate by sharing information and jointly optimizing decisions, enhancing the accuracy and efficiency of the decision-making process. We first construct a sharding blockchain environment embedding with a graph encoder. Concurrently, we propose a multiagent model with decoder, which enables agents to cooperative learning to optimize account allocation strategies based on real-time shard load and global system information. When implementing HydraChain, we address two technical challenges: 1) to extract granular behavioral features from accounts with diverse and time-varying patterns, we construct a sharding blockchain that embeds a graph encoder to build a transaction graph; and 2) to ensure real-time load balancing under the constraints of dynamic transaction patterns, we propose SG-MAPPO, which matches graph encoding features within the environment. Our approach leverages the ability of multiagent model to collaborate and adapt to the changing environment, enabling efficient resource allocation and improved system performance. Moreover, we implement HydraChain and conduct experiments on a high-performance server equipped with 48 cores and 125 GB of memory. Our comprehensive experiments, comparing HydraChain with DQN-Based, SAC-Based and SPRING, reveal that our solution outperforms the state-of-the-art methods by achieving a notable 22% increase in transaction throughput and a 5.2% reduction in workload imbalance across shards.