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Efficient Heuristic Replication Techniques for High Data Availability in Cloud

H. L. Chandrakala1,*, R. Loganathan2

1 Department of Computer Science and Engineering, School of Engineering, Presidency University, India
2 Department of Computer Science and Engineering, HKBK College of Engineering, India

* Corresponding Author: H. L. Chandrakala. Email: email, email

Computer Systems Science and Engineering 2023, 45(3), 3151-3164.


Most social networks allow connections amongst many people based on shared interests. Social networks have to offer shared data like videos, photos with minimum latency to the group, which could be challenging as the storage cost has to be minimized and hence entire data replication is not a solution. The replication of data across a network of read-intensive can potentially lead to increased savings in cost and energy and reduce the end-user’s response time. Though simple and adaptive replication strategies exist, the solution is non-deterministic; the replicas of the data need to be optimized to the data usability, performance, and stability of the application systems. To resolve the non-deterministic issue of replication, metaheuristics are applied. In this work, Harmony Search and Tabu Search algorithms are used optimizing the replication process. A novel Harmony-Tabu search is proposed for effective placement and replication of data. Experiments on large datasets show the effectiveness of the proposed technique. It is seen that the bandwidth saving for proposed harmony-Tabu replication performs better in the range of 3.57% to 18.18% for varying number of cloud datacenters when compared to simple replication, Tabu replication and Harmony replication algorithm.


Cite This Article

H. L. Chandrakala and R. Loganathan, "Efficient heuristic replication techniques for high data availability in cloud," Computer Systems Science and Engineering, vol. 45, no.3, pp. 3151–3164, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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