Choosing a Suitable Consensus Algorithm for Blockchain Applications: A Review of Factors and Challenges

Authors

  • Rajat Jain Symbiosis Institute of Technology Nagpur campus, Symbiosis International (Deemed University), Pune, India. Nagpur, India
  • Pradnya Borkar Symbiosis Institute of Technology Nagpur campus, Symbiosis International (Deemed University), Pune, India. Nagpur, India
  • Priyanshu Deshmukh Symbiosis Institute of Technology Nagpur campus, Symbiosis International (Deemed University), Pune, India. Nagpur, India
  • Sagarkumar Badhiye Symbiosis Institute of Technology Nagpur campus, Symbiosis International (Deemed University), Pune, India. Nagpur, India
  • Kritika Nimje Symbiosis Institute of Technology Nagpur campus, Symbiosis International (Deemed University), Pune, India. Nagpur, India
  • Kapil Gupta St. Vincent Pallotti College of Engineering and Technology, Nagpur Nagpur, India

Keywords:

consensus algorithms, blockchain, Byzantine fault tolerance, scalability, performance, security, CAP theorem

Abstract

Consensus algorithms are essential for ensuring the security, reliability, and performance of blockchain systems, which are distributed ledgers that store and process transactions among multiple nodes. However, choosing a suitable consensus algorithm for a blockchain application is a challenging task, as different algorithms have different trade-offs and limitations in terms of scalability, performance, and security. This paper reviews and compares various consensus algorithms, such as Byzantine fault tolerance (BFT), practical Byzantine fault tolerance (PBFT), and their improved variants, based on multiple criteria, such as goals, power consumption, cost, CAP theorem, application scenarios, and research directions. The paper provides a comprehensive overview of the current state and future challenges of consensus algorithms for blockchain technology and offers some guidelines and recommendations for selecting the best algorithm for different blockchain applications.

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References

Arati Baliga. Understanding blockchain consensus models.

Ethan Buchman, Jae Kwon, and Zarko Milosevic. The latest gossip on bft consensus. arXiv preprint arXiv:1807.04938, 2018.

Press Information Bureau. Raksha mantri us secretary of defence to co-chair india-us 2+2 ministerial dialogue hold bilateral talks on november10, 2023. Ministry of Defence, 2023.

Miguel Castro and Barbara Liskov. Practical byzantine fault tolerance.In Proceedings of the Third Symposium on Operating SystemsDesign-and Implementation, page 173–186. USENIX Association, 2001.

Stefano De Angelis, Leonardo Aniello, Roberto Baldoni, Federico Lombardi, Andrea Margheri, and Vladimiro Sassone. Pbft vs proof- of-authority: Applying the cap theorem to permissioned blockchain. In Italian Conference on Cyber Security, 2018.

Diego Geroni. Byzantine fault tolerance - a complete guide. 101 Blockchains, 2021.

Suyash Gupta, Jelle Hellings, Sajjad Rahnama, and Mohammad

Sadoghi. An in-depth look of bft consensus in blockchain: Challenges

and opportunities. In 2019 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), page 370–377. IEEE, 2019.

Zainab Hussein, Mohamed A Salama, and Sherif A El-Rahman. Evo-lution of blockchain consensus algorithms: a review on the latest milestones of blockchain consensus algorithms. Cybersecurity, 6(30),2023.

Wangxi Jiang, Xiang Wu, Ming Song, Jie Qin, and Zhen Jia. Improved pbft algorithm based on comprehensive evaluation model. Applied

Sciences, 13(2):1117, 2023.

Mohammad Ayoub Khan, Lina Ge, Jie Wang, and Guifen Zhang. Survey of consensus algorithms for proof of stake in blockchain. Security and Communication Networks, 2022:2812526, 2022.

Martin Kleppmann. A critique of the cap theorem. arXiv preprint arXiv:1509.05393, 2017.

Leslie Lamport, Robert Shostak, and Marshall Pease. The byzantine generals problem. ACM Transactions on Programming Languages and

Systems (TOPLAS), 4(3):382–401, 1982.

Leslie Lamport, Robert Shostak, and Marshall Pease. The part-time parliament. ACM Transactions on Computer Systems (TOCS),

(2):133–169, 1998.

Caitie McCaffrey. The verification of a distributed system: A practi-tioner’s guide to increasing confidence in system correctness.

Milvus. Raft or not? the best solution to data consistency in cloud.

https://milvus.io/blog/raft-or-not.md, 2023.

Kara Mostefa, Abdelkader Laouid, Muath Alshaikh, Mohammad Ham- moudeh, Ahc`ene Bounceur, Abdelfattah ammamra, and Brahim Laouid. A compute and wait in pow (cw-pow) consensus algorithm for preserving energy consumption. Applied Sciences, 11, 07 2021.

Diego Ongaro and John Ousterhout. In search of an understandable consensus algorithm. In 2014 USENIX annual technical conference (USENIX ATC 14), pages 305–319, 2014.

Ranjeet Patel. Byzantine fault tolerance (bft) and its significance in blockchain world.

Deepak Puthal, Saraju P Mohanty, Priyadarsi Nanda, Elias Kougianos, and Gautam Das. Proof-of-authentication for scalable blockchain in

resource-constrained distributed systems. IEEE Transactions on Indus- trial Informatics, 16(9):6083–6091, 2020.

Raft. Raft consensus algorithm. https://raft.github.io/, 2013.

Robbert Van Renesse and Deniz Altinbuken. Paxos made moderately

complex. ACM Computing Surveys (CSUR), 47(3):1–36, 2015.

Wenbing Zhao. Building dependable distributed systems. John Wiley & Sons, 2014.

Zibin Zheng, Shaoan Xie, Hongning Dai, Xiangping Chen, and Huaimin Wang. An overview of blockchain technology: Architecture, consensus, and future trends. In 2017 IEEE International Congress on Big Data (BigData Congress), pages 557–564, 2017.

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Published

07.01.2024

How to Cite

Jain, R. ., Borkar, P. ., Deshmukh, P. ., Badhiye, S. ., Nimje, K. ., & Gupta, K. . (2024). Choosing a Suitable Consensus Algorithm for Blockchain Applications: A Review of Factors and Challenges . International Journal of Intelligent Systems and Applications in Engineering, 12(10s), 333–341. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4381

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Research Article