China Computer Federation











执行主席:  周晓方     CCF 苏州分部



Title: BLOCKBENCH: A Framework for Analyzing Private Blockchains

Speaker: Beng Chin

Bio: Beng Chin is a Distinguished Professor of Computer Science, NGS faculty member and Director of IDMI at the National University of Singapore (NUS), and an adjunct Chang Jiang Professor at Zhejiang University. He obtained his BSc (1st Class Honors) and PhD from Monash University, Australia, in 1985 and 1989 respectively. His research interests include database, distributed processing, and large scale analytics, in the aspects of system architectures, performance issues, security, accuracy and correctness.

Beng Chin has served as a PC member for international conferences such as ACM SIGMOD, VLDB, IEEE ICDE, WWW, and SIGKDD, and as Vice PC Chair for ICDE'00,04,06, PC co-Chair for SSD'93 and DASFAA'05, PC Chair for ACM SIGMOD'07, Core DB PC chair for VLDB'08, and PC co-Chair for IEEE ICDE'12 and IEEE Big Data'15. He is serving as a PC Chair for IEEE ICDE'18. He was an editor of VLDB Journal and IEEE Transactions on Knowledge and Data Engineering, Editor-in-Chief of IEEE Transactions on Knowledge and Data Engineering (TKDE)(2009-2012), Elsevier's co-Editor-in-Chief of Journal of Big Data Research (2013-2015), and a co-chair of the ACM SIGMOD Jim Gray Best Thesis Award committee. He is serving as an editor of IEEE Transactions on Cloud Computing and Springer's Distributed and Parallel Databases. He is also serving as a Trustee Board Member and President of VLDB Endowment, and an Advisory Board Member of ACM SIGMOD. He co-founded yzBigData (hhtp:// in 2012 for Big Data Management and analytics, and Shentilium ( in 2016 for AI- and data-driven Finance Data Analytics.

Beng Chin was the recipient of ACM SIGMOD 2009 Contributions award, a co-winner of the 2011 Singapore President's Science Award, the recipient of 2012 IEEE Computer Society Kanai award, 2013 NUS Outstanding Researcher Award, 2014 IEEE TCDE CSEE Impact Award, and 2016 China Computer Federation (CCF) Overseas Outstanding Contributions Award.. He is a fellow of the ACM , IEEE, and Singapore National Academy of Science (SNAS).

Abstract: Blockchain technologies are taking the world by storm. Public blockchains, such as Bitcoin and Ethereum, enable secure peer-to-peer applications like crypto-currency or smart contracts. Private blockchain systems, on the other hand, target and aim to disrupt applications which have so far been implemented on top of database systems, for example banking, finance and trading applications. Multiple platforms for private blockchains are being actively developed and fine tuned. However, there is a clear lack of a systematic framework with which different systems can be analyzed and compared against each other. Such a framework can be used to assess blockchains' viability as another distributed data processing platform, while helping developers to identify bottlenecks and accordingly improve their platforms.

In this talk, we first describe Blockbench, the first evaluation framework for analyzing private blockchains. It serves as a fair means of comparison for different platforms and enables deeper understanding of different system design choices. Any private blockchain can be integrated to Blockbench via simple APIs and benchmarked against workloads that are based on real and synthetic smart contracts. Blockbench measures overall and component-wise performance in terms of throughput, latency, scalability and fault-tolerance. Next, we use Blockbench to conduct comprehensive evaluation of three major private blockchains: Ethereum, Parity and Hyperledger Fabric. The results demonstrate that these systems are still far from displacing current database systems in traditional data processing workloads. Furthermore, there are gaps in performance among the three systems which are attributed to the design choices at different layers of the blockchain's software stack. I will also briefly introduce our distributed storage system, UStore, which has been designed to support Private Blockchain and Collborative Analytics as spplications.