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

TL;DR

This paper introduces PSMOA, a novel multi-objective optimization algorithm designed for decentralized data replication, effectively balancing diverse organizational policies and outperforming existing algorithms in convergence and solution quality.

Contribution

The paper presents PSMOA, a new algorithm that dynamically adapts to various organizational policies for data replication, improving optimization performance over existing methods.

Findings

PSMOA outperforms NSGA-II and NSGA-III in convergence metrics.

PSMOA effectively balances multiple organizational policies.

Experimental results demonstrate superior solution quality.

Abstract

Efficient data replication in decentralized storage systems must account for diverse policies, especially in multi-organizational, 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 NSGA-III 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.