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Deep reinforcement learning for comprehensive route optimization in elastic optical networks using generative strategies

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Abstract

The latest advances in Deeper Reinforcement Learning (DRL) have completely changed how decision-making and automatic control issues are solved. The study community increasingly applies DRL methods to networking-related optimization issues like routing. Previous suggestions, though, frequently came short of conventional routing methods and could not produce satisfactory outcomes. Because of the constant development of one network efficiency parameter at the cost of individuals, most conventional safeguarding and restoring techniques will become ineffective. We believe that collectively considering the primary network parameters will be more advantageous for thorough network efficiency optimization. Additionally, elastic optical networking (EONS)’ highly adaptive characteristics necessitate the development of innovative machine learning-driven systems that adjust to the constantly changing nature of operations to execute the analysis. This study investigates how to develop DRL agents for resolving a route optimization issue using a generative strategy (GS). Our research findings indicate DRL agents operate better when employing our unique description.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Vellore Institute of Technology, Chennai Campus, Chennai, Tamilnadu, India

    P. N. Renjith

  2. Kommuri Pratap Reddy Institute of Technology, Ghanpur (Village), Ghatkeshar, Medchal, Telangana, India

    G. Sujatha

  3. Department of Electronics and Communication Engineering, K. Ramakrishnan College of Engineering, Trichy, India

    M. Vinoth

  4. Department of ECE, St Joseph’s College of Engineering, Chennai, Tamilnadu, 600119, India

    G. D. Vignesh

  5. Department of ECE, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamilnadu, India

    M. Ramkumar Prabhu

  6. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation Vaddeswaram, Guntur Dt, Andhra Pradesh, India

    B. Mouleswararao

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  1. P. N. Renjith
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  2. G. Sujatha
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  3. M. Vinoth
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  4. G. D. Vignesh
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  5. M. Ramkumar Prabhu
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  6. B. Mouleswararao
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Contributions

RPN: Contributed to conceptualization, literature review, data analysis, and manuscript writing. Mrs. GS: Provided guidance, conceptualization, methodology development, and manuscript revisions. Dr. MV: Assisted with research design, data collection, analysis, and manuscript revisions. GDV: Involved in data preprocessing, algorithm implementation, visualization, and manuscript writing. RPM: Supported in data collection, AI algorithm implementation, evaluation, and manuscript revisions. BM: Assisted with data analysis, visualization, and manuscript writing.

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Correspondence to P. N. Renjith.

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Renjith, P.N., Sujatha, G., Vinoth, M. et al. Deep reinforcement learning for comprehensive route optimization in elastic optical networks using generative strategies. Opt Quant Electron 55, 1197 (2023). https://doi.org/10.1007/s11082-023-05501-5

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