Work

PSMOA: Policy Support Multi-Objective Optimization Algorithm for Decentralized Data Replication

Year: 2025

Type: conference-paper

Abstract: Efficient data replication in decentralized storage systems must account for diverse policies, especially in multiorganizational, data-intensive environments. This work proposes PSMOA, a novel Policy Support Multi-objective Optimization Algorithm for decentralized data replication that dynamically adapts to varying organizational requirements such as minimization or maximization of replication time, storage cost, replication based on content popularity, and load balancing while respecting policy constraints. PSMOA outperforms NSGA-II and NSGAIII in both Generational Distance (20.29 vs 148.74 and 67.74) and Inverted Generational Distance (0.78 vs 3.76 and 5.61), indicating better convergence and solution distribution. These results validate PSMOA's novelty in optimizing data replication in multi-organizational environments.

Source: International Conference on Communications

DOI: 10.1109/icc52391.2025.11160807

Authors: Xi Wang, Susmit Shannigrahi

Institution: Tennessee Technological University

Language: English

Topic: Advanced Multi-Objective Optimization Algorithms

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Science Foundation

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