Article Details

Abstract

With the popularization of blockchain technology, the surge in transaction volume poses challenges to network performance. Traditional segmentation methods based on account transactions perform poorly when dealing with unbalanced data, which leads to an increase in cross-segmentation transactions and affects performance. In response to this situation, a Deep Traversal Re-segmentation Algorithm based on transaction frequency is proposed. This algorithm reduces cross-segmentation transactions through initial segmentation, and then conducts deep traversal re-segmentation on the segmentation with dense transactions to solve the problem of data imbalance. By calculating the transaction frequency of accounts, it ensures load balancing and attempts to allocate accounts with frequent transactions to the same segmentation as much as possible. Experimental results show that this algorithm can effectively balance the load, reduce cross-segmentation transactions, and improve network throughput and efficiency.

Keywords

blockchain technology; Deep Traversal Re-segmentation Algorithm; transaction frequency; cross-segmentation transaction

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About the authors

Zhao Peng (1973-), male, Han nationality, from Taiyuan, Shanxi, professor, doctoral student, research direction: software engineering, big data, blockchain; Li Jun (1998-), male, Han ethnicity, from Lvliang, Shanxi, currently pursuing a master's degree in blockchain technology and consensus algorithms; Corresponding author: Lu Bo (1988-), male, Han ethnicity, from Jinzhong, Shanxi, teaching assistant, PhD, research direction: protocol security analysis, blockchain; Guo Yunze (1997-), male, Han ethnicity, from Taiyuan, Shanxi, is currently pursuing a master's degree in blockchain technology and cryptography; Chen Wei (1996-), male, Han ethnicity, from Jinzhong, Shanxi Province, is currently pursuing a master's degree in quantum information.