Secure and Scalable Data Replication Strategies in Distributed Storage Networks

Authors

  • Suresh Bysani Venkata Naga Engineering Leader SAAS and Distributed systems Cohesity, San Francisco Bay Area, California, USA. Author
  • Krishna Chaitanya Sunkara Technical Lead Engineer, Oracle, Raleigh, North Carolina. USA. Author
  • Senthilkumar Thangavel Staff Engineer | Paypal Inc, Distributed Systems, Cloud Solutions & Machine Learning Expert, San Francisco Bay Area California, USA. Author
  • Ramakrishnan Sundaram AIML Lead Engineer | Software Architect with expertise in Big Data, Parallel processing and Distributed Systems Fremont, California, USA. Author

DOI:

https://doi.org/10.63282/3050-9416.IJAIBDCMS-V2I2P103

Keywords:

Data Replication, Distributed Storage, Security, Scalability, Blockchain, Optimization

Abstract

Data replication can be defined as one of the most vital approaches in the case of distributed storage networks and is used for achieving reliability, availability and performance. Nevertheless, future issues are still real, including security threats, expansion limitations, and optimization inconveniences. This paper researches secure and scalable data replication, focusing on a comparative analysis of the techniques and their performance index based on analytical and scientific data. Encryption, access control, and blockchain-based ways of ascertaining the integrity of a message are included in this study to address the security issue while minimizing resource consumption and redundancy. The findings in the simulation show that by applying the proposed method, high availability can indeed be achieved while at the same time having a very low latency as compared to the existing ones with improved security

References

1. Bernstein, P. A., Hadzilacos, V., & Goodman, N. (1987). Concurrency control and recovery in database systems (Vol. 370). Reading: Addison-Wesley.

2. Gray, J., Helland, P., O'Neil, P., & Shasha, D. (1996, June). The dangers of replication and a solution. In Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data (pp. 173-182).

3. Vogels, W. (2009). Eventually consistent. Communications of the ACM, 52(1), 40-44.

4. Attiya, H., Bar-Noy, A., & Dolev, D. (1995). Sharing memory robustly in message-passing systems. Journal of the ACM (JACM), 42(1), 124-142.

5. Weatherspoon, H., & Kubiatowicz, J. D. (2002, March). Erasure coding vs. replication: A quantitative comparison. In International Workshop on Peer-to-Peer Systems (pp. 328-337). Berlin, Heidelberg: Springer Berlin Heidelberg.

6. Dimakis, A. G., Godfrey, P. B., Wu, Y., Wainwright, M. J., & Ramachandran, K. (2010). Network coding for distributed storage systems. IEEE transactions on information theory, 56(9), 4539-4551.

7. Rabin, M. O. (1989). Efficient dispersal of information for security, load balancing, and fault tolerance. Journal of the ACM (JACM), 36(2), 335-348.

8. Diffie, W., & Hellman, M. E. (2022). New directions in cryptography. In Democratizing Cryptography: The Work of Whitfield Diffie and Martin Hellman (pp. 365-390).

9. Gentry, C. (2009, May). Fully homomorphic encryption using ideal lattices. In Proceedings of the forty-first annual ACM symposium on Theory of computing (pp. 169-178).

10. Sahai, A., & Waters, B. (2005). Fuzzy identity-based encryption. In Advances in Cryptology–EUROCRYPT 2005: 24th Annual International Conference on the Theory and Applications of Cryptographic Techniques, Aarhus, Denmark, May 22-26, 2005. Proceedings 24 (pp. 457-473). Springer Berlin Heidelberg.

11. Goel, S., & Buyya, R. (2007). Data replication strategies in wide-area distributed systems. In Enterprise service computing: from concept to deployment (pp. 211-241). IGI Global.

12. Jiang, H., Shen, F., Chen, S., Li, K. C., & Jeong, Y. S. (2015). A secure and scalable storage system for aggregate data in IoT. Future Generation Computer Systems, 49, 133-141.

13. Bonvin, N., Papaioannou, T. G., & Aberer, K. (2010, June). A self-organized, fault-tolerant, and scalable replication scheme for cloud storage. In Proceedings of the 1st ACM symposium on Cloud computing (pp. 205-216).

14. Du, Z., Pang, X., & Qian, H. (2021). Partitionchain: A scalable and reliable data storage strategy for permissioned blockchain. IEEE Transactions on Knowledge and Data Engineering, 35(4), 4124-4136.

15. Milani, B. A., & Navimipour, N. J. (2016). A comprehensive review of the data replication techniques in the cloud environments: Major trends and future directions. Journal of Network and Computer Applications, 64, 229-238.

16. Souravlas, S., & Sifaleras, A. (2019). Trends in data replication strategies: a survey. International Journal of Parallel, Emergent and Distributed Systems, 34(2), 222-239.

17. Amjad, T., Sher, M., & Daud, A. (2012). A survey of dynamic replication strategies for improving data availability in data grids. Future Generation Computer Systems, 28(2), 337-349.

18. Lamehamedi, H., Szymanski, B., Shentu, Z., & Deelman, E. (2002, October). Data replication strategies in grid environments. In Fifth International Conference on Algorithms and Architectures for Parallel Processing, 2002. Proceedings. (pp. 378-383). IEEE.

19. Sankarasubramanian, P. (2017). Data Security and Replication on Cloud.

20. Rashid, F., Miri, A., & Woungang, I. (2012, July). A secure data deduplication framework for cloud environments. In 2012 Tenth Annual International Conference on Privacy, Security and Trust (pp. 81-87). IEEE.

21. Sun, X., Wang, G., Xu, L., & Yuan, H. (2021). Data replication techniques in the Internet of Things: a systematic literature review. Library Hi Tech, 39(4), 1121-1136.

Downloads

Published

2021-06-30

Issue

Section

Articles

How to Cite

1.
Venkata Naga SB, Sunkara KC, Thangavel S, Sundaram R. Secure and Scalable Data Replication Strategies in Distributed Storage Networks. IJAIBDCMS [Internet]. 2021 Jun. 30 [cited 2025 Sep. 14];2(2):18-27. Available from: https://ijaibdcms.org/index.php/ijaibdcms/article/view/111