LB-Louvain: Balancing Transaction Locality and Shard Load in Account-Based State Sharding
Account-based state sharding improves blockchain parallelism by distributing account states and transaction execution across physical shards, but effective placement must preserve transaction locality without concentrating processing demand on a small number of shards. This paper presents LB-Louvain, a coarse-to-fine account-partitioning heuristic in which standard Louvain first extracts logical communities, followed by load-aware community-to-shard assignment and restricted boundary-account refinement. In controlled BlockEmulator experiments using 300,000 replayed Ethereum transactions and five repeated system runs per configuration, relative to CLPA, the complete LB-Louvain pipeline reduces the mean cross-shard transaction ratio by approximately 2.1%, increases active throughput by approximately 19.7%, and reduces average confirmation latency by approximately 9.4%. Component-wise ablation confirms complementary roles for load-aware assignment and boundary refinement. The evaluated behavior remains stable over β∈[1,4], while partition-only profiling keeps the measured partition computation below 100 ms across the tested 50,000–300,000 transaction prefixes and 4–24 physical shards. An auxiliary migration experiment further shows that Fine-Grained activation reduces the observed migration-deferred set by approximately 39.8% relative to Full Locking while maintaining comparable throughput and confirmation latency. Broader archived experiments with Monoxide and CLPA are retained separately from the controlled revision results.
Authors
- Kuan Fan (ORCID: https://orcid.org/0000-0001-7209-282X)
- Jiahui Du (ORCID: https://orcid.org/0000-0002-5641-5331)
- Zhen Luan (ORCID: https://orcid.org/0009-0005-9717-4692)
Institutions
- Qinhuangdao Science and Technology Bureau (CN)
- Northeastern University (CN)
Publication Details
- Journal
- Applied Sciences
- Published
- 2026-09-09
- DOI
- https://doi.org/10.3390/app16188959
- Primary Topic
- Cloud Computing and Resource Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00