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Batch Transaction Processing for Adaptive Sharding Blockchain-Enabled Edge Computing

delete2025-11-01
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PRE
AI
樊玉琦 (Yuqi Fan) *
B
Bo Gao
D
Dong Sheng
Z
Zipeng Hu
X
Xu Ding
DOI:10.1145/3764927delete
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Abstract

Abstract

En 中文
Edge computing (EC) provides an efficient and low-latency computing architecture for mobile multimedia communications. Blockchain-enabled EC can offer enhanced security and data privacy protection in the system, whereas throughput remains a big concern for the blockchain. Sharding is a promising solution to increase the throughput at the cost of complex cross-shard transaction verification. The popular twophase commit protocol (2PC) can ensure the consistency of cross-shard transaction processing. However, in the existing schemes based on 2PC, the number of intra-shard consensus invocations is proportional to the number of transactions, which imposes a great challenge on the system throughput and adaptivity improvement in sharding blockchains under dynamic transaction processing demands and capacities. In this article, we propose a transaction processing scheme based on 2PC, such that multiple transactions can be simultaneously processed in a batch during every execution of the consensus. Furthermore, we model the problem of transaction allocation to batches as a communication load balancing problem, aiming to balance the inter-shard communications within each batch under the shard processing capacity constraint. We also propose an effective Batch Transaction Processing algorithm (BTP) for the problem. Theoretical analysis proves that BTP is a 3-approximation algorithm for the communication load balancing problem. In the simulations and experiments on BlockEmulator, BTP respectively improves the system throughput and total transaction processing time by at least 29.41% and 22.64% over the state-of-the-art cross-shard transaction processing schemes, which demonstrates the superior adaptivity performance of BTP.
Keywords:
sharding blockchain
throughput
cross-shard transaction
two-phase commit protocol
batch processing

Journal

A
ACM Transactions on Autonomous and Adaptive Systems
IF:
2.1
Papers:
24
Citations:
378

Organization

H
Hefei University of Technology
Scholars:
5.4K
Papers: 1.8K
Citations: 2.1W