Spectrum fragmentation mitigation in elastic optical networks: a chronologically structured review using a 7-point analytical framework
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Srishty Purnasha
Abstract
Elastic optical networks (EONs) play a critical role in enabling scalable, flexible, and high-capacity optical communication to meet rising data demands. However, their dynamic spectrum allocation paradigm leads to spectrum fragmentation, resulting in inefficient resource utilization and degraded network performance. This study presents a chronologically structured review of fragmentation mitigation techniques using a 7-point analysis strategy, encompassing optimization strategies, technical methodologies, simulation topologies, assessment metrics, empirical findings, strengths, constraints and trade-offs. The analysis traces the shift from heuristic and static strategies toward adaptive metaheuristic, multipath-aware, and learning-driven approaches, alongside the emergence of specialized metrics and architectural refinements. Through the visualized performance and deployability timelines, the study reveals how innovation has progressed and where practical implementation remains challenged by centralized assumptions and limited physical-layer modeling. The findings provide a consolidated perspective on the evolution of fragmentation control and highlight future directions toward real-time adaptability, architectural scalability, and physically grounded optimization in EONs.
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Research ethics: This manuscript presents a chronologically structured analytical review of spectrum fragmentation mitigation techniques in elastic optical networks (EONs), offering an original synthesis of optimization strategies, technical methodologies, and evaluation findings through a unified seven-point analytical framework and visualized performance– deployability timelines to trace methodological evolution and implementation feasibility. This is an unpublished and original review work and is not submitted anywhere else for publication.
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Informed consent: Not applicable.
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Author contributions: Srishty Purnasha conceptualized the study, designed the analytical framework, performed the structured literature analysis, developed the visual timelines, and prepared the manuscript. Neetu Mourya supported the sourcing and organization of relevant research materials and provided feedbacks on manuscript drafts. Sneha Srivastava contributed with document formatting, layout refinement, and language review. Ashutosh Kumar Singh coordinated the manuscript preparation as well as submission process, reviewed and approved the final version of the manuscript.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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