BlockLoader: A Comprehensive Evaluation Framework for Blockchain Performance Under Various Workload Patterns

Gang Wang, Yanfeng Zhang (), Chenhao Ying, Qinnan Zhang, Zhiyuan Peng, Xiaohua Li and Ge Yu
Additional contact information
Gang Wang: School of Computer Science and Engineering, Northeastern University, Shenyang 110167, China
Yanfeng Zhang: School of Computer Science and Engineering, Northeastern University, Shenyang 110167, China
Chenhao Ying: Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Qinnan Zhang: Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100191, China
Zhiyuan Peng: Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Xiaohua Li: School of Computer Science and Engineering, Northeastern University, Shenyang 110167, China
Ge Yu: School of Computer Science and Engineering, Northeastern University, Shenyang 110167, China

Mathematics, 2024, vol. 12, issue 21, 1-19

Abstract: Hyperledger Fabric is one of the most popular permissioned blockchain platforms widely adopted in enterprise blockchain solutions. To optimize and fully utilize the platform, it is desired to conduct a thorough performance analysis of Hyperledger Fabric. Although numerous studies have analyzed the performance of Hyperledger Fabric, three significant limitations still exist. First, existing blockchain performance evaluation frameworks rely on fixed workload rates, which fail to accurately reflect the performance of blockchain systems in real-world application scenarios. Second, the impact of extending the breadth and depth of endorsement policies on the performance of blockchain systems has yet to be adequately studied. Finally, the impact of node crashes and recoveries on blockchain system performance has yet to be comprehensively investigated. To address these limitations, we propose a framework called BlockLoader, which offers seven different distributions of load rates, including linear, single-peak, and multi-peak patterns. Next, we employ the BlockLoader framework to analyze the impact of endorsement policy breadth and depth on blockchain performance, both qualitatively and quantitatively. Additionally, we investigate the impact of dynamic node changes on performance. The experimental results demonstrate that different endorsement policies exert distinct effects on performance regarding breadth and depth scalability. In the horizontal expansion of endorsement policies, the OR endorsement policy demonstrates stable performance, fluctuating around 88 TPS, indicating that adding organizations and nodes has minimal impact. In contrast, the AND endorsement policy exhibits a declining trend in performance as the number of organizations and nodes increases, with an average decrease of 10 TPS for each additional organization. Moreover, the dynamic behaviour of nodes exerts varying impacts across these endorsement policies. Specifically, under the AND endorsement policy, dynamic changes in nodes significantly affect system performance. The TPS of the AND endorsement policy shows a notable decline, dropping from 79.6 at 100 s to 41.96 at 500 s, reflecting a reduction of approximately 47% over time. Under the OR endorsement policy, the system performance remains almost unaffected.

Keywords: hyperledger fabric; BlockLoader; endorsement policy; node dynamic changes (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/21/3403/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/21/3403/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:21:p:3403-:d:1510862

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().