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Anticipating network failures and congestion in optical networks a data analytics approach using genetic algorithm optimization

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Abstract

Congestion-level forecasting is essential for optical links to function properly with networking management schemes. Effective dynamical routing and wavelengths assigning (EDRWA) difficulties under the presumptions of perfect and non-ideal structural levels represent some of the foremost challenging optimization challenges faced in constructing 3rd-generation optical links. Such two issues are each NP-complete by design. They are challenging since the search space contains several nearby optima. Heuristics-dependent methods cannot effectively handle such challenges because of the irregular nature of searching space. The route and spectral assigning problems under various demanding scenarios are solved in this study using a genetic method optimization method for creating reliable optical cores. In contexts with stochastic unpredictable needs, this enhances network efficiency. The model outcomes show how successful genetic algorithms are at optimizing dedicated optical spectrum allocation in the face of uncertainty.

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This data set is available from the Purdue University Research Repository (PURR).

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Funding

This research was supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number (PNURSP2023R359), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, MLR Institute of Technology, Dundigal, Hyderabad, Telangana, 500043, India

    Ajmeera Kiran

  2. Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India

    V. Kalpana

  3. Department of Computer Science, College of Arts and Applied Sciences, Dhofar University, Salalah, Sultanate of Oman

    Mukesh Madanan

  4. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur District, Andhra Pradesh, 522502, India

    Janjhyam Venkata Naga Ramesh

  5. Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia

    Badria Sulaiman Alfurhood

  6. Department of ECE, CMR Technical Campus, Hyderabad, Telangana, India

    Suraya Mubeen

Authors
  1. Ajmeera Kiran
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  2. V. Kalpana
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  3. Mukesh Madanan
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  4. Janjhyam Venkata Naga Ramesh
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  5. Badria Sulaiman Alfurhood
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  6. Suraya Mubeen
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Contributions

AK: Contributed to conceptualization, literature review, data analysis, and manuscript writing. VK: Provided guidance, conceptualization, methodology development, and manuscript revisions. MM: Assisted with research design, data collection, analysis, and manuscript revisions. JVNR: Involved in data preprocessing, algorithm implementation, visualization, and manuscript writing. BSA: Supported in data collection, AI algorithm implementation, evaluation, and manuscript revisions. SM: Assisted with data analysis, visualization, and manuscript writing.

Corresponding author

Correspondence to Janjhyam Venkata Naga Ramesh.

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Kiran, A., Kalpana, V., Madanan, M. et al. Anticipating network failures and congestion in optical networks a data analytics approach using genetic algorithm optimization. Opt Quant Electron 55, 1193 (2023). https://doi.org/10.1007/s11082-023-05480-7

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  • DOI: https://doi.org/10.1007/s11082-023-05480-7

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