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BLOCKBENCH: A Framework for Analyzing Private Blockchains

Published: 09 May 2017 Publication History

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. Their security and performance are well studied. This paper concerns recent private blockchain systems designed with stronger security (trust) assumption and performance requirement. These systems 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 paper, 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. We have released BLOCKBENCH for public use.

References

[1]
BlockBench: private blockchains benchmarking. https://github.com/ooibc88/blockbench.
[2]
Ethereum blockchain app platform. https://www.ethereum.org/.
[3]
Ibm watson iot. http://www.ibm.com/internet-of-things.
[4]
Leveldb. https://leveldb.org.
[5]
Monax: The ecosystem application platform. https://monax.io.
[6]
Rocksdb. https://rocksdb.org.
[7]
M. Apostolaki, A. Zohar, and L. Vanbever. Hijacking bitcoin: Large-scale network attacks on crypto-currencies. https://arxiv.org/abs/1605.07524, 2016.
[8]
P. Bailis, A. Fekete, M. J. Franklin, A. Ghodsi, J. M. Hellerstein, and I. Stoica. Coordination avoidance in database systems. In VLDB, 2014.
[9]
J. Bonneau, A. Miller, J. Clark, A. Narayanan, J. A. Kroll, and E. W. Felten. Sok: Research perspectives and challenges for bitcoin and crypto-currencies. In 2015 IEEE Symposium on Security and Privacy, pages 104--121. IEEE, 2015.
[10]
M. Cahill, U. Rohm, and A. D. Fekete. Serializable isolation for snapshot databases. In SIGMOD, 2008.
[11]
M. Castro and B. Liskov. Practical byzantine fault tolerance. In Proceedings of the third symposium on Operating systems design and implementation, pages 173--186. USENIX Association, 1999.
[12]
B.-G. Chun, P. Maniatis, S. Shenker, and J. Kubiatowicz. Attested append-only memory: Making adversaries stick to their word. In SOSP, 2007.
[13]
B. F. Cooper, A. Silberstein, E. Tam, R. Ramakrishnan, and R. Sears. Benchmarking cloud serving systems with ycsb. In SoCC, 2010.
[14]
J. C. Corbett and J. D. et al. Spanner: Google's globally-distributed database. In OSDI, 2012.
[15]
K. Croman, C. Decker, I. Eyal, A. E. Gencer, A. Juels, A. Kosba, A. Miller, P. Saxena, E. Shi, and E. Gün. On scaling decentralized blockchains. In Proc. 3rd Workshop on Bitcoin and Blockchain Research, 2016.
[16]
Crypti. A decentralized application platform. https://crypti.me.
[17]
C. Decker and R. Wattenhofer. Information propagation in bitcoin network. In P2P, 2013.
[18]
D. E. Difallah, A. Pavlo, C. Curino, and P. Cudre-Mauroux. Oltp-bench: An extensible testbed for benchmarking relational databases. In VLDB, 2013.
[19]
A. Dinh, J. Wang, S. Wang, W.-N. Chin, Q. Lin, B. C. Ooi, P. Ruan, K.-L. Tan, Z. Xie, H. Zhang, and M. Zhang. UStore: a distributed storage with rich semantics. https://arxiv.org/pdf/1702.02799.pdf.
[20]
J. Douceur. The sybil attack. In IPTPS, 2002.
[21]
A. Dragojevic, D. Narayanan, E. B. Nightingale, M. Renzelmann, A. Shamis, A. Badam, and M. Castro. No compromises: distributed transactions with consistency, availability and performance. In SOSP, 2015.
[22]
Ethcore. Parity: next generation ethereum browser. https://ethcore.io/parity.html.
[23]
Ethcore. Performance analysis. https://blog.ethcore.io/performance-analysis/.
[24]
Ethereum. Ethereum benchmarks. https://github.com/ethereum/wiki/wiki/Benchmarks.
[25]
I. Eyal, A. E. Gencer, E. G. Sirer, and R. van Renesse. Bitcoin-ng: A scalable blockchain protocol. In NSDI, 2016.
[26]
I. Eyal and E. G. Sirer. Majority is not enough: Bitcoin mining is vulnerable. In Fiancial Cryptography, 2014.
[27]
A. Gervais, G. O. Karame, K. Wust, V. Glykantizis, H. Ritzdorf, and S. Capkun. On the security and performance of proof of work blockchains. https://eprint.iacr.org/2016/555.pdf.
[28]
A. Ghazal, T. Rabl, M. Hu, F. Raab, M. Poess, A. Crolotte, and H.-A. Jacobsen. Bigbench: towards an industry standard benchmark for big data analytics. In SIGMOD, 2013.
[29]
G. S. Group. Blockchain: putting theory into practice, 2016.
[30]
E. Heilman, A. Kendler, A. Zohar, and S. Goldberg. Eclipse attacks on Bitcoin's peer-to-peer network. In USENIX Security, 2015.
[31]
Hyperledger. Blockchain technologies for business. https://www.hyperledger.org.
[32]
Intel. Hibench suite. https://github.com/intel-hadoop/HiBench.
[33]
F. P. Junqueira, B. C. Reed, and M. Serafini. Zab: high-performance broadcast for primary-backup systems. In Dependable Systems and Networks, 2011.
[34]
E. Kokoris-Kogias, P. Jovanovic, N. Gailly, I. Khoffi, L. Gasser, and B. Ford. Enhancing bitcoin security and performance with strong consistency via collective signing. In USENIX Security, 2016.
[35]
L. Lamport. Paxos made simple. SIGACT News, 2001.
[36]
Q. Lin, P. Chang, G. Chen, B. C. Ooi, K.-L. Tan, and Z. Wang. Towards a non-2pc transaction management in distrubted database systems. In SIGMOD, 2016.
[37]
L. Luu, V. Narayanan, C. Zhang, K. Baweija, S. Gilbert, and P. Saxena. A secure sharding protocol for open blockchains. In CCS, 2016.
[38]
L. Luu, J. Teutsch, R. Kulkarni, and P. Saxena. Demystifying Incentives in the Consensus Computer. CCS '15, pages 706--719, 2015.
[39]
Melonport. Blockchain software for asset management. http://melonport.com.
[40]
J. Morgan and O. Wyman. Unlocking economic advantage with blockchain. a guide for asset managers., 2016.
[41]
S. Nakamoto. Bitcoin: A peer-to-peer electronic cash system, 2008.
[42]
D. Ongaro and J. Ousterhout. In search of an understandable consensus algorithm. In USENIX ATC, 2014.
[43]
R. Pass and E. Shi. Hybrid consensus: efficient consensus in the permissionless model. https://eprint.iacr.org/2016/917.pdf.
[44]
Ripple. Ripple. https://ripple.com.
[45]
Y. Sompolinsky and A. Zohar. Accelerating bitcoin's transaction processing: fast money grows on trees, not chains. Cryptology ePrint Archive, Report 2013/881, 2013. https://eprint.iacr.org/2013/881.pdf.
[46]
M. Stonebraker, S. Madden, D. J. Abadi, S. Harizopoulos, N. Hachem, and P. Helland. The end of and architectural era (it's time for a complete rewrite). In VLDB, 2007.
[47]
K.-L. Tan, Q. Cai, B. C. Ooi, W.-F. Wong, C. Yao, and H. Zhang. In-memory databases: Challenges and opportunities from software and hardware perspectives. SIGMOD Records, 44(2), 2015.
[48]
A. Thomson, T. Diamond, S. chun Weng, K. Ren, P. Shao, and D. J. Abadi. Calvin: fast distributed transaction for partitioned database systems. In SIGMOD, 2012.
[49]
Q. H. Vu, M. Lupu, and B. C. Ooi. Peer-to-Peer Computing Principles and Applications. Springer-Verlag, 2009.
[50]
M. Vukolic. The quest for scalable blockchain fabric: proof-of-work vs. bft replication. In Open Problems in Network Security - iNetSec, 2015.
[51]
H. Zhang, G. Chen, B. C. Ooi, K.-L. Tan, and M. Zhang. In-memory big data management and processing: a survey. TKDE, 2015.

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Published In

cover image ACM Conferences
SIGMOD '17: Proceedings of the 2017 ACM International Conference on Management of Data
May 2017
1810 pages
ISBN:9781450341974
DOI:10.1145/3035918
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 09 May 2017

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Author Tags

  1. blockchains
  2. consensus
  3. performance benchmark
  4. security
  5. smart contracts
  6. transactions

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  • Research-article

Funding Sources

  • National Natural Science Foundation of China
  • National Research Foundation Prime Minister's Office Singapore

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SIGMOD/PODS'17
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Overall Acceptance Rate 785 of 4,003 submissions, 20%

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