<scp>DH</scp> ‐Chain: Double Heap Based Blockchain Sharding Framework

ABSTRACT Blockchain sharding has emerged as a promising solution to address the scalability challenges of modern blockchain systems, yet in practice, existing sharding systems still suffer from serious performance bottlenecks. In recent work, LB‐Chain proposes a framework for load balancing by dynamically migrating hot accounts, which significantly improves throughput and latency. However, the approach still suffers from the computational overhead associated with frequent full ordering, which limits its efficiency in large‐scale systems. To address this issue, this paper proposes an enhanced sharding framework, DH‐Chain, which improves computational efficiency by introducing heap sorting optimization, dynamically managing the load of sharding and accounts, and avoiding full‐volume sorting during each round of migration. The experimental results show that DH‐Chain achieves 3.2% higher throughput than LB‐Chain and 11.4% higher than random allocation, approaching the theoretical upper bound of ideal allocation. By leveraging heap‐based sorting and two‐phase commit protocols, DH‐Chain ensures atomicity and security while reducing computational overhead. The framework effectively balances shard loads, maintaining consistent performance across varying transaction loads and demonstrating robust scalability.

Authors

Institutions

Publication Details

Journal
Concurrency and Computation Practice and Experience
Published
2026-02-01
DOI
https://doi.org/10.1002/cpe.70586
Primary Topic
Blockchain Technology Applications and Security
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

DH ‐Chain: Double Heap Based Blockchain Sharding Framework

Jingjing Gao, Qi Xia, Hu Xia, Chen Wang
Concurrency and Computation Practice and Experience
Blockchain Technology Applications and Security
article

DH ‐Chain: Double Heap Based Blockchain Sharding Framework

Jingjing Gao, Qi Xia, Hu Xia, Chen Wang
article en

Abstract

ABSTRACT Blockchain sharding has emerged as a promising solution to address the scalability challenges of modern blockchain systems, yet in practice, existing sharding systems still suffer from serious performance bottlenecks. In recent work, LB‐Chain proposes a framework for load balancing by dynamically migrating hot accounts, which significantly improves throughput and latency. However, the approach still suffers from the computational overhead associated with frequent full ordering, which limits its efficiency in large‐scale systems. To address this issue, this paper proposes an enhanced sharding framework, DH‐Chain, which improves computational efficiency by introducing heap sorting optimization, dynamically managing the load of sharding and accounts, and avoiding full‐volume sorting during each round of migration. The experimental results show that DH‐Chain achieves 3.2% higher throughput than LB‐Chain and 11.4% higher than random allocation, approaching the theoretical upper bound of ideal allocation. By leveraging heap‐based sorting and two‐phase commit protocols, DH‐Chain ensures atomicity and security while reducing computational overhead. The framework effectively balances shard loads, maintaining consistent performance across varying transaction loads and demonstrating robust scalability.

Concurrency and Computation Practice and ExperienceVol. 38(3)
University of Electronic Science and Technology of China (CN), Space Engineering University (CN)
Reduced inequalities
Openalex Percentile: Top 76%
Blockchain Technology Applications and Security
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.