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Space division multiplexed elastic optical networks (SDM-EONs) enhance service provisioning by offering increased fiber capacity through the use of flexible spectrum allocation, multiple spatial modes, and efficient modulations. In these networks, the problem of allocating resources for connections involves assigning routes, modulations, cores, and spectrum (RMCSA). However, the presence of intercore crosstalk (XT) between ongoing connections on adjacent cores can degrade signal transmission, necessitating proper handling during resource assignment. The use of multiple modulations in translucent optical networks presents a challenge in balancing spectrum utilization and XT accumulation. In this paper, we propose a dual-optimized RMCSA algorithm called the Capacity Loss Aware Resource Assignment Algorithm (CLARA+), which optimizes network capacity utilization to improve resource availability and network performance. A two-step machine-learning-enabled optimization is used to improve the resource allocations by balancing the tradeoff between spectrum utilization and XT accumulation with the help of feature extraction from the network. Extensive simulations demonstrate that CLARA+ significantly reduces bandwidth blocking probability and enhances resource utilization across various scenarios. We show that our strategy applied to a few algorithms from the literature improves the bandwidth blocking probability by up to three orders of magnitude. The algorithm effectively balances spectrum utilization and XT accumulation more efficiently compared to existing algorithms in the literature.
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Machine Learning-based Spectrum Resource Assignment and End-to-End Path Reconfiguration for Flexible Optical Networks
Optical resource management and path reconfiguration have become increasingly paramount in the era of dynamic and flexible optical networks. This paper presents machine learning-based optical resource assignment for highly efficient resource utilization in Spatial Division Multiplexing (SDM) networks. We also demonstrate a network orchestration with a Whitebox-based optical network and failure prediction in an SDM network.
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- Award ID(s):
- 2210384
- PAR ID:
- 10646968
- Publisher / Repository:
- IEEE
- Date Published:
- ISSN:
- 2161-2064
- ISBN:
- 979-8-3503-7732-3
- Page Range / eLocation ID:
- 1 to 4
- Format(s):
- Medium: X
- Location:
- Bari, Italy
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICTON62926.2024.10648205
