research-article

AdaChain: A Learned Adaptive Blockchain

Authors:

Mohammad Javad Amiri,

Boon Thau LooAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 16, Issue 8

Pages 2033 - 2046

https://doi.org/10.14778/3594512.3594531

Published: 01 April 2023 Publication History

Abstract

This paper presents AdaChain, a learning-based blockchain framework that adaptively chooses the best permissioned blockchain architecture to optimize effective throughput for dynamic transaction workloads. AdaChain addresses the challenge in Blockchain-as-a-Service (BaaS) environments, where a large variety of possible smart contracts are deployed with different workload characteristics. AdaChain supports automatically adapting to an underlying, dynamically changing workload through the use of reinforcement learning. When a promising architecture is identified, AdaChain switches from the current architecture to the promising one at runtime in a secure and correct manner. Experimentally, we show that AdaChain can converge quickly to optimal architectures under changing workloads and significantly outperform fixed architectures in terms of the number of successfully committed transactions, all while incurring low additional overhead.

References

[1]

[n. d.]. https://github.com/google/leveldb

[2]

[n. d.]. AWS. Amazon quantum ledger database (QLDB). https://aws.amazon.com/qldb/.

[3]

[n. d.]. Blockchain on AWS Enterprise blockchain made real. https://aws.amazon.com/blockchain/.

[4]

[n. d.]. Chain. http://chain.com.

[5]

[n. d.]. Corda. https://github.com/corda/corda.

[6]

[n. d.]. Hyperledger Caliper. https://www.hyperledger.org/use/caliper.

[7]

[n. d.]. Hyperledger Iroha. https://github.com/hyperledger/iroha.

[8]

[n. d.]. IBM Blockchain Platform. https://www.ibm.com/cloud/blockchain-platform.

[9]

[n. d.]. Oracle Blockchain. https://www.oracle.com/blockchain/.

[10]

Shipra Agrawal and Navin Goyal. 2013. Further Optimal Regret Bounds for Thompson Sampling. In The International Conference on Artificial Intelligence and Statistics (AISTATS '13).

[11]

Yair Amir, Brian Coan, Jonathan Kirsch, and John Lane. 2011. Prime: Byzantine replication under attack. Transactions on Dependable and Secure Computing 8, 4 (2011), 564--577.

Digital Library

[12]

Mohammad Javad Amiri, Divyakant Agrawal, and Amr El Abbadi. 2019. Par-Blockchain: Leveraging Transaction Parallelism in Permissioned Blockchain Systems. In Int. Conf. on Distributed Computing Systems (ICDCS). IEEE, 1337--1347.

[13]

Mohammad Javad Amiri, Divyakant Agrawal, and Amr El Abbadi. 2021. SharPer: Sharding Permissioned Blockchains Over Network Clusters. In SIGMOD Int. Conf. on Management of Data. ACM, 76--88.

[14]

Mohammad Javad Amiri, Joris Duguépéroux, Tristan Allard, Divyakant Agrawal, and Amr El Abbadi. 2021. SEPAR: Towards Regulating Future of Work Multi-Platform Crowdworking Environments with Privacy Guarantees. In Proceedings of The Web Conf. (WWW). 1891--1903.

Digital Library

[15]

Mohammad Javad Amiri, Boon Thau Loo, Divyakant Agrawal, and Amr El Abbadi. 2022. Qanaat: A Scalable Multi-Enterprise Permissioned Blockchain System with Confidentiality Guarantees. Proc. of the VLDB Endowment 15, 11 (2022), 2839--2852.

Digital Library

[16]

Elli Androulaki, Artem Barger, Vita Bortnikov, Christian Cachin, et al. 2018. Hyperledger Fabric: a distributed operating system for permissioned blockchains. In European Conf. on Computer Systems (EuroSys). ACM, 30:1--30:15.

Digital Library

[17]

Leo Breiman. 1996. Bagging Predictors. In Machine Learning (Maching Learning '96).

[18]

Leo Breiman. 2001. Random Forests. Machine Learning 45, 1 (Oct. 2001), 5--32.

Digital Library

[19]

Wei Cao, Feifei Li, Gui Huang, Jianghang Lou, Jianwei Zhao, Dengcheng He, Mengshi Sun, Yingqiang Zhang, Sheng Wang, Xueqiang Wu, et al. 2022. PolarDBX: An Elastic Distributed Relational Database for Cloud-Native Applications. In 2022 IEEE 38th International Conference on Data Engineering (ICDE). IEEE, 2859--2872.

[20]

Miguel Castro, Barbara Liskov, et al. 1999. Practical Byzantine fault tolerance. In Symposium on Operating systems design and implementation (OSDI), Vol. 99. USENIX Association, 173--186.

[21]

Lujing Cen, Ryan Marcus, Hongzi Mao, Justin Gottschlich, Mohammad Alizadeh, and Tim Kraska. 2020. Learned Garbage Collection. In Proceedings of the 4th ACM SIGPLAN International Workshop on Machine Learning and Programming Languages (MAPL @ PLDI '20). ACM.

Digital Library

[22]

Jeeta Ann Chacko, Ruben Mayer, and Hans-Arno Jacobsen. 2021. Why Do My Blockchain Transactions Fail? A Study of Hyperledger Fabric. In SIGMOD Int. Conf. on Management of Data. ACM, 221--234.

[23]

Olivier Chapelle and Lihong Li. 2011. An empirical evaluation of Thompson sampling. In Advances in neural information processing systems (NIPS'11).

[24]

JP Morgan Chase. 2016. Quorum white paper.

[25]

Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song. [n. d.]. Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning. arXiv:1712.05526 [cs] http://arxiv.org/abs/1712.05526 type: article.

[26]

Sam Daley. 2021. 18 Blockchain-as-a-Service Companies Making the DLT More Accessible. https://builtin.com/blockchain/blockchain-as-a-service-companies.

[27]

Sudipto Das, Shoji Nishimura, Divyakant Agrawal, and Amr El Abbadi. 2011. Albatross: Lightweight elasticity in shared storage databases for the cloud using live data migration. Proceedings of the VLDB Endowment 4, 8 (2011), 494--505.

Digital Library

[28]

Bailu Ding, Sudipto Das, Ryan Marcus, Wentao Wu, Surajit Chaudhuri, and Vivek R. Narasayya. 2019. AI Meets AI: Leveraging Query Executions to Improve Index Recommendations. In 38th ACM Special Interest Group in Data Management (SIGMOD '19).

Digital Library

[29]

Tien Tuan Anh Dinh, Ji Wang, Gang Chen, Rui Liu, Beng Chin Ooi, and Kian-Lee Tan. 2017. Blockbench: A framework for analyzing private blockchains. In SIGMOD Int. Conf. on Management of Data. ACM, 1085--1100.

Digital Library

[30]

Dmitry Duplyakin, Robert Ricci, Aleksander Maricq, Gary Wong, Jonathon Duerig, Eric Eide, Leigh Stoller, Mike Hibler, David Johnson, Kirk Webb, et al. 2019. The Design and Operation of {CloudLab}. In Annual Technical Conf. (ATC). USENIX Association, 1--14.

[31]

Aaron J Elmore, Sudipto Das, Divyakant Agrawal, and Amr El Abbadi. 2011. Zephyr: live migration in shared nothing databases for elastic cloud platforms. In Proceedings of the 2011 ACM SIGMOD International Conference on Management of data. 301--312.

Digital Library

[32]

The Linux Fundation. 2021. netem. Retrieved July 4, 2022 from https://wiki.linuxfoundation.org/networking/netem

[33]

Zerui Ge, Dumitrel Loghin, Beng Chin Ooi, Pingcheng Ruan, and Tianwen Wang. 2022. Hybrid blockchain database systems: design and performance. Proceedings of the VLDB Endowment 15, 5 (2022), 1092--1104.

Digital Library

[34]

Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014).

[35]

Justin Gottschlich, Armando Solar-Lezama, Nesime Tatbul, Michael Carbin, Martin Rinard, Regina Barzilay, Saman Amarasinghe, Joshua B. Tenenbaum, and Tim Mattson. 2018. The three pillars of machine programming. In Proceedings of the 2nd ACM SIGPLAN International Workshop on Machine Learning and Programming Languages (MAPL 2018). Association for Computing Machinery, Philadelphia, PA, USA, 69--80.

Digital Library

[36]

Vincent Gramoli, Rachid Guerraoui, Andrei Lebedev, Chris Natoli, and Gauthier Voron. 2022. Diablo-v2: A Benchmark for Blockchain Systems. Technical Report.

[37]

Gideon Greenspan. 2015. MultiChain private blockchain-White paper. URl: http://www.multichain.com/download/MultiChain-White-Paper.pdf (2015).

[38]

Hyperledger. [n. d.]. Private Data Collections: A High-Level Overview. https://www.hyperledger.org/blog/2018/10/23/private-data-collections-a-high-level-overview.

[39]

Zsolt István, Alessandro Sorniotti, and Marko Vukolić. 2018. StreamChain: Do Blockchains Need Blocks?. In Workshop on Scalable and Resilient Infrastructures for Distributed Ledgers (SERIAL). ACM, 1--6.

Digital Library

[40]

Junbin Kang, Le Cai, Feifei Li, Xingxuan Zhou, Wei Cao, Songlu Cai, and Daming Shao. 2022. Remus: Efficient Live Migration for Distributed Databases with Snapshot Isolation. In Proceedings of the 2022 International Conference on Management of Data. 2232--2245.

Digital Library

[41]

Manos Kapritsos, Yang Wang, Vivien Quema, Allen Clement, Lorenzo Alvisi, Mike Dahlin, et al. 2012. All about Eve: Execute-Verify Replication for Multi-Core Servers. In Symposium on Operating systems design and implementation (OSDI), Vol. 12. USENIX Association, 237--250.

[42]

Emilie Kaufmann, Nathaniel Korda, and Rémi Munos. 2012. Thompson sampling: An asymptotically optimal finite-time analysis. In International Conference on Algorithmic Learning Theory (ALT '12).

Digital Library

[43]

Andreas Kipf, Thomas Kipf, Bernhard Radke, Viktor Leis, Peter Boncz, and Alfons Kemper. 2019. Learned Cardinalities: Estimating Correlated Joins with Deep Learning. In 9th Biennial Conference on Innovative Data Systems Research (CIDR '19). http://arxiv.org/abs/1809.00677

[44]

Tim Kraska, Alex Beutel, Ed H. Chi, Jeffrey Dean, and Neoklis Polyzotis. 2018. The Case for Learned Index Structures. In Proceedings of the 2018 International Conference on Management of Data (SIGMOD '18). ACM, New York, NY, USA.

Digital Library

[45]

Alexey Kurakin, Ian Goodfellow, and Samy Bengio. 2016. Adversarial machine learning at scale. arXiv preprint arXiv:1611.01236 (2016).

[46]

Jae Kwon. 2014. Tendermint: Consensus without mining. (2014).

[47]

Yu-Shan Lin, Shao-Kan Pi, Meng-Kai Liao, Ching Tsai, Aaron Elmore, and Shan-HungWu. 2019. MgCrab: transaction crabbing for live migration in deterministic database systems. Proceedings of the VLDB Endowment 12, 5 (2019), 597--610.

Digital Library

[48]

Henry Liu, Mingbin Xu, Ziting Yu, Vincent Corvinelli, and Calisto Zuzarte. 2015. Cardinality Estimation Using Neural Networks. In Proceedings of the 25th Annual International Conference on Computer Science and Software Engineering (CASCON '15). IBM Corp., Riverton, NJ, USA, 53--59. http://dl.acm.org/citation.cfm?id=2886444.2886453

Digital Library

[49]

Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2018. Towards Deep Learning Models Resistant to Adversarial Attacks. In International Conference on Learning Representations.

[50]

Hongzi Mao, Malte Schwarzkopf, Shaileshh Bojja Venkatakrishnan, Zili Meng, and Mohammad Alizadeh. 2018. Learning Scheduling Algorithms for Data Processing Clusters. arXiv:1810.01963 [cs, stat] (2018). http://arxiv.org/abs/1810.01963 arXiv: 1810.01963.

[51]

Ryan Marcus, Parimarjan Negi, Hongzi Mao, Nesime Tatbul, Mohammad Alizadeh, and Tim Kraska. 2021. Bao: Making Learned Query Optimization Practical. In Proceedings of the 2021 International Conference on Management of Data (SIGMOD '21). China. Award: 'best paper award'.

Digital Library

[52]

Ryan Marcus, Parimarjan Negi, Hongzi Mao, Chi Zhang, Mohammad Alizadeh, Tim Kraska, Olga Papaemmanouil, and Nesime Tatbul. 2019. Neo: A Learned Query Optimizer. PVLDB 12, 11 (2019), 1705--1718.

Digital Library

[53]

Diego Ongaro and John K Ousterhout. 2014. In search of an understandable consensus algorithm. In Annual Technical Conf. (ATC). USENIX Association, 305--319.

[54]

Ian Osband and Benjamin Van Roy. 2015. Bootstrapped Thompson Sampling and Deep Exploration. arXiv:1507.00300 [cs, stat] (July 2015). http://arxiv.org/abs/1507.00300

[55]

Andrew Pavlo, Gustavo Angulo, Joy Arulraj, Haibin Lin, Jiexi Lin, Lin Ma, Prashanth Menon, Todd C. Mowry, Matthew Perron, Ian Quah, Siddharth Santurkar, Anthony Tomasic, Skye Toor, Dana Van Aken, Ziqi Wang, Yingjun Wu, Ran Xian, and Tieying Zhang. 2017. Self-Driving Database Management Systems. In 8th Biennial Conference on Innovative Data Systems Research (CIDR '17).

[56]

Zhe Peng, Cheng Xu, Haixin Wang, Jinbin Huang, Jianliang Xu, and Xiaowen Chu. 2021. P2B-Trace: Privacy-Preserving Blockchain-based Contact Tracing to Combat Pandemics. In SIGMOD Int. Conf. on Management of Data. 2389--2393.

[57]

Zhe Peng, Jianliang Xu, Xiaowen Chu, Shang Gao, Yuan Yao, Rong Gu, and Yuzhe Tang. 2021. Vfchain: Enabling verifiable and auditable federated learning via blockchain systems. IEEE Transactions on Network Science and Engineering (2021).

[58]

Pingcheng Ruan, Dumitrel Loghin, Quang-Trung Ta, Meihui Zhang, Gang Chen, and Beng Chin Ooi. 2020. A Transactional Perspective on Execute-order-validate Blockchains. In SIGMOD Int. Conf. on Management of Data. ACM, 543--557.

Digital Library

[59]

Ankur Sharma, Felix Martin Schuhknecht, Divya Agrawal, and Jens Dittrich. 2019. Blurring the lines between blockchains and database systems: the case of hyperledger fabric. In SIGMOD Int. Conf. on Management of Data. ACM, 105--122.

Digital Library

[60]

Feng Tian. 2017. A supply chain traceability system for food safety based on HACCP, blockchain & Internet of things. In Int. Conf. on service systems and service management (ICSSSM). IEEE, 1--6.

[61]

Shin-Yeh Tsai, Yizhou Shan, and Yiying Zhang. 2020. Disaggregating Persistent Memory and Controlling Them Remotely: An Exploration of Passive Disaggregated {Key-Value} Stores. In Annual Technical Conf. (ATC). USENIX Association, 33--48.

[62]

Jia-Chen Wang, Ding Ding, Huan Wang, Conrad Christensen, Zhaoguo Wang, Haibo Chen, and Jinyang Li. 2021. Polyjuice: High-Performance Transactions via Learned Concurrency Control. In OSDI. 198--216.

[63]

Eric Wong, Leslie Rice, and J Zico Kolter. 2019. Fast is better than free: Revisiting adversarial training. In International Conference on Learning Representations.

[64]

Chenyuan Wu, Mohammad Javad Amiri, Jared Asch, Heena Nagda, Qizhen Zhang, and Boon Thau Loo. 2022. FlexChain: An Elastic Disaggregated Blockchain. Proc. of the VLDB Endowment 16, 01 (2022), 23--36.

Digital Library

[65]

Xinying Yang, Yuan Zhang, Sheng Wang, Benquan Yu, Feifei Li, Yize Li, and Wenyuan Yan. 2020. LedgerDB: a centralized ledger database for universal audit and verification. Proceedings of the VLDB Endowment 13, 12 (2020), 3138--3151.

Digital Library

[66]

Li Zhou. 2016. A Survey on Contextual Multi-armed Bandits. arXiv:1508.03326 [cs] (Feb. 2016). http://arxiv.org/abs/1508.03326

Cited By

Wu CAmiri MQin HMehta BMarcus RLoo B(2024)Towards Full Stack Adaptivity in Permissioned BlockchainsProceedings of the VLDB Endowment10.14778/3641204.364121617:5(1073-1080)Online publication date: 2-May-2024
https://dl.acm.org/doi/10.14778/3641204.3641216
Wu CQin HJavad Amiri MThau Loo BMalkhi DMarcus R(2024)Towards Truly Adaptive Byzantine Fault-Tolerant ConsensusACM SIGOPS Operating Systems Review10.1145/3689051.368905558:1(15-22)Online publication date: 14-Aug-2024
https://dl.acm.org/doi/10.1145/3689051.3689055
Song JJeong JLee JNa IKim MSerra ESpezzano F(2024)HTFabric: A Fast Re-ordering and Parallel Re-execution Method for a High-Throughput BlockchainProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679606(2118-2127)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679606
Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 16, Issue 8

April 2023

257 pages

ISSN:2150-8097

Editors:
Georgia Koutrika
Athena Research Center
,
Jun Yang
Duke University

Issue’s Table of Contents

Publisher

VLDB Endowment

Publication History

Published: 01 April 2023

Published in PVLDB Volume 16, Issue 8

Check for updates

Badges

Artifacts Available / v1.1

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

7
Total Citations
View Citations
95
Total Downloads

Downloads (Last 12 months)69
Downloads (Last 6 weeks)6

Reflects downloads up to 19 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Wu CAmiri MQin HMehta BMarcus RLoo B(2024)Towards Full Stack Adaptivity in Permissioned BlockchainsProceedings of the VLDB Endowment10.14778/3641204.364121617:5(1073-1080)Online publication date: 2-May-2024
https://dl.acm.org/doi/10.14778/3641204.3641216
Wu CQin HJavad Amiri MThau Loo BMalkhi DMarcus R(2024)Towards Truly Adaptive Byzantine Fault-Tolerant ConsensusACM SIGOPS Operating Systems Review10.1145/3689051.368905558:1(15-22)Online publication date: 14-Aug-2024
https://dl.acm.org/doi/10.1145/3689051.3689055
Song JJeong JLee JNa IKim MSerra ESpezzano F(2024)HTFabric: A Fast Re-ordering and Parallel Re-execution Method for a High-Throughput BlockchainProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679606(2118-2127)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679606
Zhang SXiao JWu ECheng FLi BWang WJin H(2024)MorphDAG: A Workload-Aware Elastic DAG-Based BlockchainIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.338274336:10(5249-5264)Online publication date: Oct-2024
https://doi.org/10.1109/TKDE.2024.3382743
Li CQiu WLi XLiu CZheng Z(2024)A Dynamic Adaptive Framework for Practical Byzantine Fault Tolerance Consensus Protocol in the Internet of ThingsIEEE Transactions on Computers10.1109/TC.2024.337792173:7(1669-1682)Online publication date: Jul-2024
https://doi.org/10.1109/TC.2024.3377921
Lin JDeng RLu ZZhang YDuan Q(2024)TuneChain: An Online Configuration Auto-Tuning Approach for Permissioned Blockchain Systems2024 IEEE International Conference on Web Services (ICWS)10.1109/ICWS62655.2024.00072(512-523)Online publication date: 7-Jul-2024
https://doi.org/10.1109/ICWS62655.2024.00072
Debreczeni MKlenik AKocsis I(2024)Transaction Conflict Control in Hyperledger Fabric: A Taxonomy, Gaps, and Design for Conflict PreventionIEEE Access10.1109/ACCESS.2024.336131812(18987-19008)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3361318

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents