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Data replication in distributed systems using olympiad optimization algorithm
Journal article   Open access

Data replication in distributed systems using olympiad optimization algorithm

Bahman Arasteh, Asgarali Bouyer, Reza Ghanbarzadeh, Alireza Rouhi, Mahsa Nazeri Mehrabani and Erfan Babaee Tirkolaee
Facta Universitatis. Series: Mechanical Engineering, Vol.21(3), pp.501-527
31/10/2023
DOI: https://doi.org/10.22190/FUME230707033A
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Abstract

Olympiad optimization algorithm Distributed systems Replica placement Data access time Stability Availability
Achieving timely access to data objects is a major challenge in big distributed systems like the Internet of Things (IoT) platforms. Therefore, minimizing the data read and write operation time in distributed systems has elevated to a higher priority for system designers and mechanical engineers. Replication and the appropriate placement of the replicas on the most accessible data servers is a problem of NP-complete optimization. The key objectives of the current study are minimizing the data access time, reducing the quantity of replicas, and improving the data availability. The current paper employs the Olympiad Optimization Algorithm (OOA) as a novel population-based and discrete heuristic algorithm to solve the replica placement problem which is also applicable to other fields such as mechanical and computer engineering design problems. This discrete algorithm was inspired by the learning process of student groups who are preparing for the Olympiad exams. The proposed algorithm, which is divide-and-conquer-based with local and global search strategies, was used in solving the replica placement problem in a standard simulated distributed system. The 'European Union Database' (EUData) was employed to evaluate the proposed algorithm, which contains 28 nodes as servers and a network architecture in the format of a complete graph. It was revealed that the proposed technique reduces data access time by 39% with around six replicas, which is vastly superior to the earlier methods. Moreover, the standard deviation of the results of the algorithm's different executions is approximately 0.0062, which is lower than the other techniques' standard deviation within the same experiments.

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