Startseite Jointly managed congestion and contention in optical burst switching with proposed proactive framework (Pro-JCCM) for the high-speed transport networks
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Jointly managed congestion and contention in optical burst switching with proposed proactive framework (Pro-JCCM) for the high-speed transport networks

  • Amit Kumar Garg EMAIL logo
Veröffentlicht/Copyright: 29. Juli 2025
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Abstract

Optical burst switching (OBS) shows a lot of promise for the future of the optical internet, still it comes with few challenges, such as contention and congestion, which can lead to a higher burst drop probability (BDP). Existing solutions tend to solve these issues separately and reactively, which limits their effectiveness. This paper introduces a new approach called the proactive framework for joint congestion and contention management (Pro-JCCM). Pro-JCCM focuses on two main components: a network state estimator (NSE) that calculates a combined congestion and contention index (CCI) for network routes, and an adaptive burst scheduling and dropping (ABSD) module that operates at the edge nodes. By proactively monitoring the CCI, the ABSD module makes smart decisions, like adjusting burst offset times or dropping low-priority bursts before they reach the network core. Simulations conducted on the National Science Foundation Network (NSFNET) topology show that the proposed Pro-JCCM outperforms traditional reactive methods, reducing packet drop rates by up to tenfold and increasing network throughput by over 30 % during peak loads, all while ensuring consistent end-to-end delays for improved quality of service (QoS).


Corresponding author: Amit Kumar Garg, Department of Electronics & Communication Engineering, Deenbandhu Chhotu Ram University of Science & Technology, Sonipat 131039, India, E-mail:

Acknowledgments

Discrete Event Simulation Tool (network tool), NSFNET topology.

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: The author has accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: No conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

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Received: 2025-06-20
Accepted: 2025-07-10
Published Online: 2025-07-29

© 2025 Walter de Gruyter GmbH, Berlin/Boston

Heruntergeladen am 21.9.2025 von https://www.degruyterbrill.com/document/doi/10.1515/joc-2025-0252/html
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