ContribChain: A Stress-Balanced Blockchain Sharding Protocol with Node Contribution Awareness

TL;DR

ContribChain tackles shard stress in blockchain sharding by introducing node contribution values that capture both security and performance, updated epochally. It couples NACV for node allocation with P-Louvain for account allocation to align shard workloads with capabilities, achieving improved throughput and reduced cross-shard traffic. The framework is supported by theoretical analyses of contribution validity and shard security, and empirical results on real Ethereum data show substantial gains over baselines in allocation speed, TPS, and stability. This stress-balanced approach enhances scalability and resilience in heterogeneous, dynamic networks, with avenues for dynamically adjusting shard count and allocation frequency in future work.

Abstract

Existing blockchain sharding protocols have focused on eliminating imbalanced workload distributions. However, even with workload balance, disparities in processing capabilities can lead to differential stress among shards, resulting in transaction backlogs in certain shards. Therefore, achieving stress balance among shards in the dynamic and heterogeneous environment presents a significant challenge of blockchain sharding. In this paper, we propose ContribChain, a blockchain sharding protocol that can automatically be aware of node contributions to achieve stress balance. We calculate node contribution values based on the historical behavior to evaluate the performance and security of nodes. Furthermore, we propose node allocation algorithm NACV and account allocation algorithm P-Louvain, which both match shard performance with workload to achieve stress balance. Finally, we conduct extensive experiments to compare our work with state-of-the-art baselines based on real Ethereum transactions. The evaluation results show that P-Louvain reduces allocation execution time by 86% and the cross-shard transaction ratio by 7.5%. Meanwhile, ContribChain improves throughput by 35.8% and reduces the cross-shard transaction ratio by 16%.

Figures (9)

• Figure 1: The stress imbalance issue in dynamic environments. Transaction backlogs occur in shards with low processing capabilities due to the lack of consideration of shard performance during transaction and node allocation.
• Figure 2: Workflow of ContribChain during epoch $e$. In ContribChain, Phase 3 is executed every $f$ epochs.
• Figure 3: Data Structure of the System Summary Block.
• Figure 4: Data Structure of the State Block.
• Figure 5: Performance of account allocation algorithms with varying $N_{TX}$. In (a) and (b), assume the TPS of shards are {1000, 800, 800, 600}. In (c) and (d), assume the TPS of shards are {1000, 900, 900, 800, 800, 700, 700, 600}.