Preventing Data Loss using Raft Consensus Algorithm in a Decentralized Database System

Hafiz, Md. Muhtaseen; Zaman, A.K.M Nafiz; Shaf, Md Shadman

dc.contributor.author	Hafiz, Md. Muhtaseen
dc.contributor.author	Zaman, A.K.M Nafiz
dc.contributor.author	Shaf, Md Shadman
dc.date.accessioned	2024-01-18T07:09:41Z
dc.date.available	2024-01-18T07:09:41Z
dc.date.issued	2023-05-30
dc.identifier.citation	[1] S. Liu and R. Kuhn, “Data loss prevention,” IT professional, vol. 12, no. 2, pp. 10–13, 2010. [2] L. Cheng, F. Liu, and D. Yao, “Enterprise data breach: Causes, challenges, prevention, and future directions,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 7, no. 5, e1211, 2017. [3] R. Van Renesse and F. B. Schneider, “Chain replication for supporting high throughput and availability.,” in OSDI, vol. 4, 2004. [4] B. Calder, J. Wang, A. Ogus, et al., “Windows azure storage: A highly available cloud storage service with strong consistency,” in Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles, 2011, pp. 143–157. [5] A. Adya, W. J. Bolosky, M. Castro, et al., “Farsite: Federated, available, and reliable storage for an incompletely trusted environment,” ACM SIGOPS Operating Systems Review, vol. 36, no. SI, pp. 1–14, 2002. [6] F. B. Schneider, “Implementing fault-tolerant services using the state ma chine approach: A tutorial,” ACM Computing Surveys (CSUR), vol. 22, no. 4, pp. 299–319, 1990. [7] V. Iancu and I. Ignat, “A peer-to-peer consensus algorithm to enable storage reliability for a decentralized distributed database,” in 2010 IEEE Interna tional Conference on Automation, Quality and Testing, Robotics (AQTR), vol. 2, 2010, pp. 1–6. doi: 10.1109/AQTR.2010.5520830. [8] M. K. Lai and K. Schildkamp, “Data-based decision making: An overview,” Data-based decision making in education: Challenges and opportunities, pp. 9– 21, 2013. [9] R. S. Sandhu, “On five definitions of data integrity.,” in DBSec, Citeseer, 1993, pp. 257–267. 60 [10] G. Sivathanu, C. P. Wright, and E. Zadok, “Ensuring data integrity in stor age: Techniques and applications,” in Proceedings of the 2005 ACM workshop on Storage security and survivability, 2005, pp. 26–36. [11] G. J. Matthews and O. Harel, “Data confidentiality: A review of methods for statistical disclosure limitation and methods for assessing privacy,” 2011. [12] B. Liu, X. L. Yu, S. Chen, X. Xu, and L. Zhu, “Blockchain based data integrity service framework for iot data,” in 2017 IEEE International Con ference on Web Services (ICWS), IEEE, 2017, pp. 468–475. [13] S. M. Diesburg and A.-I. A. Wang, “A survey of confidential data storage and deletion methods,” ACM Computing Surveys (CSUR), vol. 43, no. 1, pp. 1–37, 2010. [14] Z. Zhao, H. Zhao, Q. Zhuang, et al., “Efficiently supporting multi-level serial izability in decentralized database systems,” IEEE Transactions on Knowl edge and Data Engineering, 2023. [15] D. Serrano, M. Pati˜no-Martınez, R. Jim´enez-Peris, and B. Kemme, “Boost ing database replication scalability through partial replication and 1-copy snapshot-isolation,” in 13th Pacific Rim International Symposium on De pendable Computing (PRDC 2007), IEEE, 2007, pp. 290–297. [16] F. Cristian, “Understanding fault-tolerant distributed systems,” Communi cations of the ACM, vol. 34, no. 2, pp. 56–78, 1991. [17] H. Lamehamedi, B. Szymanski, Z. Shentu, and E. Deelman, “Data repli cation strategies in grid environments,” in Fifth International Conference on Algorithms and Architectures for Parallel Processing, 2002. Proceedings., IEEE, 2002, pp. 378–383. [18] S. Suguna and A. Suhasini, “Overview of data backup and disaster recovery in cloud,” in International Conference on Information Communication and Embedded Systems (ICICES2014), IEEE, 2014, pp. 1–7. 61 [19] M. Wiesmann, F. Pedone, A. Schiper, B. Kemme, and G. Alonso, “Database replication techniques: A three parameter classification,” in Proceedings 19th IEEE Symposium on Reliable Distributed Systems SRDS-2000, IEEE, 2000, pp. 206–215. [20] J. Holliday, R. Steinke, D. Agrawal, and A. El Abbadi, “Epidemic algorithms for replicated databases,” IEEE Transactions on Knowledge and Data En gineering, vol. 15, no. 5, pp. 1218–1238, 2003. [21] S. Deb, M. M´edard, and C. Choute, “Algebraic gossip: A network coding approach to optimal multiple rumor mongering,” IEEE Transactions on In formation Theory, vol. 52, no. 6, pp. 2486–2507, 2006. [22] R. Jim´enez-Peris, M. Pati˜no-Martınez, G. Alonso, and B. Kemme, “Are quo rums an alternative for data replication?” ACM Transactions on Database Systems (TODS), vol. 28, no. 3, pp. 257–294, 2003. [23] S. Nakamoto, “Bitcoin: A peer-to-peer electronic cash system,” Decentralized business review, p. 21 260, 2008. [24] D. Ongaro and J. Ousterhout, “In search of an understandable consensus al gorithm,” in 2014 {USENIX} Annual Technical Conference ({USENIX}{ATC} 14), 2014, pp. 305–319. [25] M. S´ustrik et al., “Zeromq,” Introduction Amy Brown and Greg Wilson, 2015. [26] L. H. royaltm dependabot[bot] dependabot[bot], An opinionated raft imple mentation powered by ømq, https://github.com/royaltm/node- zmq raft, 2017. [27] S. L. Fritchie, “Chain replication in theory and in practice,” in Proceedings of the 9th ACM SIGPLAN workshop on Erlang, 2010, pp. 33–44. [28] A. Papaioannou and K. Magoutis, “Addressing the read-performance impact of reconfigurations in replicated key-value stores,” IEEE Transactions on Parallel and Distributed Systems, vol. 33, no. 9, pp. 2106–2119, 2021.	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/2063
dc.description	Supervised by Mr. Faisal Hussain, Assistant Professor, Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.description.abstract	In today’s digital era, decentralized database management systems have gained significant attention due to their ability to provide scalability, fault tolerance, and improved performance. However, ensuring data integrity, preventing data loss, and maintaining data consistency in such systems remain challenging tasks. This thesis addresses these challenges by proposing a peer-to-peer gossip-based solution that leverages the Raft consensus algorithm and replicated log method. The proposed solution focuses on making each node in the database cluster a witness to transactions, allowing for consensus on the current state of the database. By utilizing gossip-based protocols, transaction information is disseminated among nodes, ensuring that updates reach all relevant participants. The Raft consensus algorithm is employed to achieve agreement on the committed transactions, while the replicated log method synchronizes transaction logs across all nodes. The objectives of this thesis include preventing data loss, maintaining data con sistency, and meeting high transaction and view request targets. With a target transaction rate of 1000 transactions per second and a target view request rate of 10000 requests per second, the solution aims to deliver robust performance and reliability. By combining the peer-to-peer gossip-based approach, Raft consensus algorithm, and replicated log method, the proposed solution offers benefits such as fault tolerance, scalability, and data consistency. The thesis contributes to the field by addressing the limitations of current database systems and proposing an innovative solution that ensures data integrity in de centralized environments. The limitations and complexities of Direct Mail, Anti Entropy, and Rumor Mongering techniques are analyzed, leading to the devel opment of a more effective and efficient solution. The solution’s architecture, mechanisms, and protocols are designed to meet the specified targets and provide a reliable foundation for decentralized database management systems. Through simulations and performance evaluations, the proposed solution demon strates its effectiveness in preventing data loss, maintaining data consistency, and meeting the specified transaction and view request targets. The results highlight the solution’s scalability, fault tolerance, and ability to handle high transaction rates. In conclusion, this thesis presents a peer-to-peer gossip-based solution that lever ages the Raft consensus algorithm and replicated log method to prevent data loss and ensure data consistency in decentralized database management systems. The solution offers a robust and scalable approach, addressing the limitations of exist ing techniques. With its potential applications in various domains, the proposed solution contributes to the advancement of decentralized database management systems, providing a foundation for reliable and high-performance data storage and processing.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.title	Preventing Data Loss using Raft Consensus Algorithm in a Decentralized Database System	en_US
dc.type	Thesis	en_US