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

2025-05-20

AI Summary

This paper addresses the dynamic multi-objective data replication optimization problem in decentralized multi-organizational storage systems, characterized by heterogeneous organizational policies, competing objectives—including replication latency, storage cost, content popularity, and load balancing—and stringent operational constraints. We propose the first evolutionary algorithm framework supporting fine-grained embedding of organizational policies into the optimization process. Built upon enhanced NSGA-II/III, our approach integrates a policy parsing engine, a dynamic weight adaptation mechanism, and a constraint-guided elitist preservation strategy to enable real-time parsing and adaptive optimization under diverse policy requirements. Experimental evaluation demonstrates significant improvements over baseline algorithms: a 20.29 reduction in Generational Distance and a 0.78 improvement in Inverted Generational Distance, confirming superior convergence, solution distribution diversity, and practical applicability in realistic multi-organizational settings.

Technology Category

• Search and Optimization: Evolutionary Computation
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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.

Problem

• Optimizes decentralized data replication with policy constraints
• Balances replication time, cost, popularity, and load
• Outperforms NSGA-II and NSGA-III in convergence metrics

Innovation

• Integrates NSGA-III with Entropy Weighted TOPSIS
• Dynamically adapts to organizational requirements
• Optimizes replication time, cost, and load balancing