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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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TRA: an efficient dynamic resource assignment algorithm for MCF-based SS-FONs
Service provisioning can be enhanced with spectrally spatially flexible optical networks (SS-FONs) with multicore fibers; however, intercore crosstalk (XT) is a dominant impairment that complicates the problem of maintaining the quality of transmission (QoT) and resource allocation. The selection of modulation formats (MFs), due to their unique XT sensitivities, further increases the complexity. The routing, modulation, core, and spectrum assignment (RMCSA) problem must select the resources carefully to exploit the available capacity while meeting the desired QoT. In this paper, we propose an RMCSA algorithm called the tridental resource assignment (TRA) algorithm for transparent SS-FONs, and its variant, translucency-aware TRA (TaTRA), for translucent SS-FONs. TRA balances three different factors that affect network performance under dynamic resource allocation. We consider translucent networks with flexible regeneration and with and without modulation and spectrum conversion. Our resource assignment approach includes both an offline network planning component to calculate path priorities and an online/dynamic provisioning component to allocate resources. Extensive simulation experiments performed in realistic network scenarios indicate that TRA and TaTRA significantly reduce the bandwidth blocking probability by several orders of magnitude in some cases.
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- PAR ID:
- 10369222
- Publisher / Repository:
- Optical Society of America
- Date Published:
- Journal Name:
- Journal of Optical Communications and Networking
- Volume:
- 14
- Issue:
- 7
- ISSN:
- 1943-0620; JOCNBB
- Format(s):
- Medium: X Size: Article No. 511
- Size(s):
- Article No. 511
- Sponsoring Org:
- National Science Foundation
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