BrokerChain: A Blockchain Sharding Protocol by Exploiting Broker Accounts

TL;DR

BrokerChain tackles hot-shard and cross-shard latency issues in account-based blockchain sharding by combining fine-grained state-graph partitioning with account segmentation and a broker-account mechanism. It introduces two shard types (P-shard and M-shard), an epoch-based PBFT framework, and a modified Shard State Tree (mSST) to enable efficient cross-shard TXs via broker-backed relay while maintaining workload balance. The approach reduces CTXs and improves throughput and latency relative to Monoxide, LBF, and Metis, with experiments showing significantly lower cross-shard TX ratios (e.g., around 7.4% vs ~98% for baselines) and higher TPS (up to ~9048 in high-shard settings). The results demonstrate practical benefits for scalable, secure state-sharded blockchains and highlight the potential for broker incentives and broader deployment.

Abstract

State-of-the-art blockchain sharding solutions such as Monoxide, can cause severely imbalanced distribution of transaction (TX) workloads across all blockchain shards due to the deployment policy of their accounts. Imbalanced TX distributions then produce hot shards, in which the cross-shard TXs may experience an unlimited confirmation latency. Thus, how to address the hot-shard issue and how to reduce crossshard TXs become significant challenges of blockchain sharding. Through reviewing the related studies, we find that a crossshard TX protocol that can achieve workload balance among all shards and simultaneously reduce the quantity of crossshard TXs is still absent from the literature. To this end, we propose BrokerChain, which is a cross-shard blockchain protocol dedicated to account-based state sharding. Essentially, BrokerChain exploits fine-grained state partition and account segmentation. We also elaborate on how BrokerChain handles cross-shard TXs through broker accounts. The security issues and other properties of BrokerChain are analyzed rigorously. Finally, we conduct comprehensive evaluations using an opensource blockchain sharding prototype named BlockEmulator. The evaluation results show that BrokerChain outperforms other baselines in terms of transaction throughput, transaction confirmation latency, the queue size of the transaction pool, and workload balance.

Figures (21)

• Figure 1: Ratio of cross-shard transactions vs the number of shards (i.e., $S$).
• Figure 2: When feeding 80000 TXs for each iteration of blockchain consensus, Monoxide's sharding protocol Wang2019Monoxide yields imbalanced transaction distributions among all shards. Consequently, some hot shards are induced. Here, we call a shard congested by an overwhelming number of TXs a hot shard.
• Figure 3: Four major phases of BrokerChain protocol in an epoch $t$. There are two types of shards in BrokerChain, i.e., the partition shard (P-shard) and mining shards (M-shard). Here, we only use two M-shards to illustrate how BrokerChain protocol works.
• Figure 4: Illustration of how blocks connect between epochs (e.g., $B^{t-1}$ is a state block generated in epoch $t$-1 but followed by the first transaction blocks of M-shards in epoch $t$).
• Figure 5: Data structure design of the state block. The modules with grey background are designed particularly for state updating.

Theorems & Definitions (9)

• Theorem 1
• proof
• Remark 1
• Theorem 2
• proof
• Remark 2
• Theorem 3
• proof
• Remark 3