TuneChain: An Online Configuration Auto-Tuning Approach for Permissioned Blockchain Systems

Junxiong Lin, Ruijun Deng, Zhihui Lu, Yiguang Zhang, Qiang Duan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The increasing prevalence of blockchain technology has drawn significant attention to the need for effective Quality of Service (QoS) management in blockchain service provision. In this context, the online tuning of system configurations is pivotal for automatic blockchain services to meet QoS requirements. Past studies on configuration tuning have primarily focused on system adaptability to hardware and network environments, overlooking the dynamic nature of the highly diverse workloads, thus resulting in suboptimal system performance. This paper presents TuneChain, an online configuration auto-tuning approach for permissioned blockchain systems, which addresses the limitations of current methods, particularly in handling dynamic workloads while minimizing tuning costs. TuneChain leverages a Conflict Emergency Mechanism (CF-EM) to mitigate the impact of transaction conflicts on effective throughput and employs the Proximal Policy Optimization (PPO) algorithm coupled with a multi-instance mechanism to offer adaptive configuration recommendations tailored to diverse workloads. Additionally, TuneChain incorporates a Tuning Causal Model (TCModel) based on expert knowledge to guide decision-making in configuration tuning, thereby reducing unnecessary exploration and improving efficiency. Extensive evaluations demonstrate that TuneChain outperforms state-of-the-art approaches to configuration tuning in adapting to dynamic workloads, showcasing its efficacy in enhancing blockchain service performance.

Original languageEnglish (US)
Title of host publicationProceedings - 2024 IEEE International Conference on Web Services, ICWS 2024
EditorsRong N. Chang, Carl K. Chang, Zigui Jiang, Jingwei Yang, Zhi Jin, Michael Sheng, Jing Fan, Kenneth K. Fletcher, Qiang He, Qiang He, Claudio Ardagna, Jian Yang, Jianwei Yin, Zhongjie Wang, Amin Beheshti, Stefano Russo, Nimanthi Atukorala, Jia Wu, Philip S. Yu, Heiko Ludwig, Stephan Reiff-Marganiec, Emma Zhang, Anca Sailer, Nicola Bena, Kuang Li, Yuji Watanabe, Tiancheng Zhao, Shangguang Wang, Zhiying Tu, Yingjie Wang, Kang Wei
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages512-523
Number of pages12
ISBN (Electronic)9798350368550
DOIs
StatePublished - 2024
Event2024 IEEE International Conference on Web Services, ICWS 2024 - Shenzhen, China
Duration: Jul 7 2024Jul 13 2024

Conference

Conference2024 IEEE International Conference on Web Services, ICWS 2024
Country/TerritoryChina
CityShenzhen
Period7/7/247/13/24

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Information Systems and Management

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