An official website of the United States government
The increasing demand for flexible and efficient optical networks has led to the development of Software-Defined Elastic Optical Networks (SD-EONs). These networks leverage the programmability of Software-Defined Networking (SDN) and the adaptability of Elastic Optical Networks (EONs) to optimize network performance under dynamic traffic conditions. However, existing simulation tools often fall short in terms of transparency, flexibility, and advanced functionality, limiting their utility in cutting-edge research. In this paper, we present a Flexible Unified Simulator for Intelligent Optical Networking (FUSION), a fully open-source simulator designed to address these limitations and provide a comprehensive platform for SD-EON research. FUSION integrates traditional routing and spectrum assignment algorithms with advanced machine learning and reinforcement learning techniques, including support for the Stable Baselines 3 library. The simulator also offers robust unit testing, a fully functional Graphical User Interface (GUI), and extensive documentation to ensure usability and reliability. Performance evaluations demonstrate the effectiveness of FUSION in modeling complex network scenarios, showcasing its potential as a powerful tool for advancing SD-EON research.
more »
« less
PRODIGY+: a robust progressive upgrade approach for elastic optical networks
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
- PAR ID:
- 10533512
- Publisher / Repository:
- Optical Society of America
- Date Published:
- Journal Name:
- Journal of Optical Communications and Networking
- Volume:
- 16
- Issue:
- 9
- ISSN:
- 1943-0620; JOCNBB
- Format(s):
- Medium: X Size: Article No. E48
- Size(s):
- Article No. E48
- 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
-
A fine-grained flexible frequency grid for elastic optical transmission and space division multiplexing in conjunction with spectrally efficient modulations is an excellent solution to the coming capacity crunch. In space division multiplexed elastic optical networks (SDM-EONs), the routing, modulation, core, and spectrum assignment (RMCSA) problem is an important lightpath resource assignment problem. Intercore cross talk (XT) reduces the quality of parallel transmissions on separate cores, and the RMCSA algorithm must ensure that XT requirements are satisfied while optimizing network performance. There is an indirect trade-off between spectrum utilization and XT tolerance; while higher modulations are more spectrum efficient, they are also less tolerant of XT since they permit fewer connections on neighboring cores on the overlapping spectra. Numerous XT-aware RMCSA algorithms restrict the number of litcores, cores on which overlapping spectra are occupied, to guarantee XT constraints are met. In this paper, we present a machine learning (ML) aided threshold optimization strategy that enhances the performance ofanyRMCSA algorithm for any network model. 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. We also present the RMCSA algorithm called spectrum-wastage-avoidance-based resource allocation (SWARM), which is based on the idea of spectrum wastage due to spectrum requirements and XT constraints. We note that SWARM not only outperforms other RMCSA algorithms, but also its ML-optimized variant outperforms other ML-optimized RMCSA algorithms.more » « less
-
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
-
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.more » « less
