Traffic demands in future elastic optical networks are expected to be heterogeneous with time-varying bandwidth. Estimating the physical-layer impairments (PLIs) for random bandwidth demands is important for cross-layer network resource provisioning. State-of-the-art PLI estimation techniques yield conservative PLI estimates using the maximum bandwidth, which leads to significant over-provisioning. This paper uses probabilistic information on random bandwidth demands to provide a computationally efficient, accurate, and flexible PLI estimate. The proposed model is consistent with the needs of future self-configuring fiber-optic networks and maximally avoids up to a 25% overestimation of PLIs compared to the benchmark for the cases studied, thus reducing the network design margin at a negligible extra computational cost.
Flexible grid networks need rigorous resource planning to avoid network over-dimensioning and resource over-provisioning. The network must provision the hardware and spectrum resources statically, even for dynamic random bandwidth demands, due to the infrastructure of flexible grid networks, hardware limitations, and reconfiguration speed of the control plane. We propose a flexible online–offline probabilistic (FOOP) algorithm for the static spectrum assignment of random bandwidth demands. The FOOP algorithm considers the probabilistic nature of random bandwidth demands and balances hardware and control plane pressures with spectrum assignment efficiency. The FOOP algorithm uses the probabilistic spectrum Gaussian noise (PSGN) model to estimate the physical-layer impairment (PLI) for random bandwidth traffic. Compared to a benchmark spectrum assignment algorithm and a widely applied PLI estimation model, the proposed FOOP algorithm using the PSGN model saves up to 49% of network resources.
more » « less- Award ID(s):
- 1718130
- NSF-PAR ID:
- 10209157
- Publisher / Repository:
- Optical Society of America
- Date Published:
- Journal Name:
- Journal of Optical Communications and Networking
- Volume:
- 13
- Issue:
- 3
- ISSN:
- 1943-0620; JOCNBB
- Page Range / eLocation ID:
- Article No. 1
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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