An official website of the United States government
GSNR-aware resource re-provisioning for C to C+L-bands upgrade in optical backbone networks
Abstract Efficient network management in optical backbone networks is essential to manage continuous traffic growth. To accommodate this growth, network operators need to upgrade their infrastructure at appropriate times. Given the cost constraint of upgrading the entire network at once, upgrading the network periodically in multiple batches is a more pragmatic approach to meet the growing demands. While multi-period, batch-upgrade strategies to increase network capacity from the conventional C band to C+L bands have been proposed, they did not consider so far the possibility to re-provision existing traffic. In this work, we investigate how to selectively re-provision connections from C band to L band during a batch upgrade. This is to ensure greater availability of C-band resources which can help to delay network upgrade and hence reduce upgrade cost, while limiting the number of disrupted connections in the network. This study proposes two re-provisioning strategies, namely, Budget-Based (BB) and Margin-Aware (MA) re-provisioning, which rely on the Quality of Transmission (QoT) of lightpaths. These strategies leverage the knowledge of Generalized Signal-to-Noise Ratio (GSNR) to choose which lightpaths to re-provision. We compare these strategies with a baseline distance-based strategy that uses path length to select and re-provision lightpaths. We also incorporate Machine Learning techniques for QoT estimation of lightpaths to reduce the computational time required for optical-path feasibility check. Numerical results show that, compared to distance-based strategy, BB and MA strategies reduce disruption by about 22% and 27%, respectively, in representative network topologies.
more »
« less
- Award ID(s):
- 2226042
- PAR ID:
- 10523187
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Photonic Network Communications
- Volume:
- 47
- Issue:
- 3
- ISSN:
- 1387-974X
- Format(s):
- Medium: X Size: p. 139-153
- Size(s):
- p. 139-153
- Sponsoring Org:
- National Science Foundation
More Like this
-
We investigate cost-efficient upgrade strategies for capacity enhancement in optical backbone networks enabled by C+L-band optical line systems. A multi-period strategy for upgrading network links from the C band to the C+L band is proposed, ensuring physical-layer awareness, cost effectiveness, and less than 0.1% blocking. Results indicate that the performance of an upgrade strategy depends on efficient selection of the sequence of links to be upgraded and on the time instant to upgrade, which are either topology or traffic dependent. Given a network topology, a set of traffic demands, and growth projections, our illustrative numerical results show that a well-devised upgrade strategy can achieve superior cost efficiency during the capacity upgrade to C+L enhancement.more » « less
-
Multi-band transmission is a promising solution for capacity enhancement in optical networks. We propose a novel strategy, named C to C+L Upgrade (CLU), to gradually upgrade links from C to C+L bands. We develop a Recurrent Neural Network (RNN)-based model to efficiently predict links for upgrade, based on network state and resource utilization, to reduce blocking and upgrade cost. Our results show that CLU outperforms baseline strategies (which do not employ predictive decisions) by upgrading fewer links at appropriate times.more » « less
-
Elastic optical networks (EONs) operating in the C-band have been widely deployed worldwide. However, two major technologies—multiband elastic optical networks (MB-EONs) and space division multiplexed elastic optical networks (SDM-EONs)—can significantly increase network capacity beyond traditional EONs. A one-time greenfield deployment of these flexible-grid technologies may not be practical, as existing investments in flexible-grid EONs need to be preserved and ongoing services must face minimal disruption. Therefore, we envision the coexistence of flexible-grid, multiband, and multicore technologies during the brownfield migration. Each technology represents a tradeoff between higher capacity and greater deployment overhead, directly impacting network performance. Moreover, as traffic demands continue rising, capacity exhaustion becomes inevitable. Considering the different characteristics of these technologies, we propose a robust network planning solution called Progressive Optics Deployment and Integration for Growing Yields (PRODIGY+) to gradually migrate current C-band EONs. PRODIGY+ employs proactive measures inspired by the Swiss Cheese Model, making the network robust to traffic peaks while meeting service level agreements. The upgrade strategy enables a gradual transition to minimize migration costs while continuously supporting increasing traffic demands. We provide a detailed comparison of our proposed PRODIGY+ strategy against baseline strategies, demonstrating its superior performance.more » « less
-
Estimating the quality of transmission (QoT) of a lightpath before its establishment is a critical procedure for efficient design and management of optical networks. Recently, supervised machine learning (ML) techniques for QoT estimation have been proposed as an effective alternative to well-established, yet approximated, analytic models that often require the introduction of conservative margins to compensate for model inaccuracies and uncertainties. Unfortunately, to ensure high estimation accuracy, the training set (i.e., the set of historical field data, or “samples,” required to train these supervised ML algorithms) must be very large, while in real network deployments, the number of monitored/monitorable lightpaths is limited by several practical considerations. This is especially true for lightpaths with an above-threshold bit error rate (BER) (i.e., malfunctioning or wrongly dimensioned lightpaths), which are infrequently observed during network operation. Samples with above-threshold BERs can be acquired by deploying probe lightpaths, but at the cost of increased operational expenditures and wastage of spectral resources. In this paper, we propose to useactive learningto reduce the number of probes needed for ML-based QoT estimation. We build an estimation model based on Gaussian processes, which allows iterative identification of those QoT instances that minimize estimation uncertainty. Numerical results using synthetically generated datasets show that, by using the proposed active learning approach, we can achieve the same performance of standard offline supervised ML methods, but with a remarkable reduction (at least 5% and up to 75%) in the number of training samples.more » « less
