PSMOA: Policy Support Multi-Objective Optimization Algorithm for Decentralized Data Replication
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916747506876416 |
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| author | Wang, Xi Shannigrahi, Susmit |
| author_facet | Wang, Xi Shannigrahi, Susmit |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_14574 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PSMOA: Policy Support Multi-Objective Optimization Algorithm for Decentralized Data Replication Wang, Xi Shannigrahi, Susmit Networking and Internet Architecture Distributed, Parallel, and Cluster Computing 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. |
| title | PSMOA: Policy Support Multi-Objective Optimization Algorithm for Decentralized Data Replication |
| topic | Networking and Internet Architecture Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2505.14574 |