Routing And Spectrum Assignment Research Articles

Pre- and post-processing techniques for reinforcement-learning-based routing and spectrum assignment in elastic optical networks

Research on the routing and spectrum assignment (RSA) problem has long been conducted with the aim of efficiently utilizing the frequency resources of optical networks. Given the recent progress in machine learning (ML) technology, it has been reported that the application of ML in various areas of optical network design, operation, and management has the potential to bring about new innovations such as autonomous optical network operation and highly accurate estimation of network conditions. With regard to the RSA problem, it is expected that an algorithm that achieves better accommodation efficiency than conventional heuristic methods can be realized by applying reinforcement learning (RL), which well supports training in a simulation environment. In this paper, we introduce and evaluate three techniques devised to apply RL more effectively to elastic optical networks (EONs): two pre-processing techniques called link-axis positional encoding (LPE) and slot-axis positional encoding (SPE) and a post-processing technique named the assignable boundary slot mask. First, we build a simple model in which the state data of the optical network, including frequency slot utilization, are input to the neural network of the RSA agent in RL and show that this model has difficulty outperforming the conventional heuristic RSA algorithm. Next, we build an RSA agent model with the proposed techniques and simulate the accommodation of dynamic optical paths to quantitatively demonstrate that the blocking probability can be reduced by 17% compared to the conventional heuristic.

Experimental evaluation of a latency-aware routing and spectrum assignment mechanism based on deep reinforcement learning

The introduction of futuristic and challenging use cases of 5G and 6G communications will demand strict requirements in terms of high bandwidth and low latency. Optical backbone networks need to tackle these new network scenarios by offering highly efficient, flexible, and scalable technologies and solutions. In this context, elastic optical networks (EONs) have been recognized as a promising technological transport infrastructure for the future Internet since they can manage the optical spectrum with enhanced flexibility and efficiency. The service provisioning in EONs is a challenging issue to tackle since the routing and spectrum assignment (RSA) is characterized by a high degree of complexity. This work presents an approach for RSA in EONs leveraging the advantages of deep reinforcement learning (DRL) solutions. The devised approach jointly considers the constraints imposed by the optical technologies and the demanded connectivity service requirements (i.e., guaranteed bandwidth and maximum end-to-end latency) when computing and selecting the optical path and spectral resources. We first evaluate our approach through simulation experiments considering two reference network topologies, demonstrating its effectiveness in reducing the bandwidth blocking ratio, the path computation time, and the number of rejected connectivity services requiring lower latencies when compared to a baseline k-shortest path routing and first-fit spectrum allocation algorithm. Then, the trained DRL agent is integrated within a real proof of concept to attain an ML-assisted SDN control plane in the CTTC ADRENALINE testbed. The attained performance improvements highlight the potential benefits brought by using DRL mechanisms and its feasible integration within production EON transport infrastructures.

Planning of Survivable Wavelength-Switched Optical Networks Based on P2MP Transceivers

Nowadays, the booming of emerging network services have shifted the major traffic pattern in metro-aggregation networks from point-to-point (P2P) to hub-and-spoke (H&S). Hence, it will be promising to plan metro-aggregation networks with point-to-multipoint coherent optical transceivers (P2MP-TRXs). This work studies how to plan a survivable wavelength-switched optical network (WSON) with P2MP-TRXs and shared backup path protection (SBPP) to address single-link failures. We formulate an integer linear programming (ILP) model to place P2MP-TRXs, assign sub-carriers (SCs) to P2MP-TRXs, and calculate routing and spectrum assignment (RSA) for the working/backup lightpath between each hub-leaf P2MP-TRX pair, such that traffic demands can be satisfied with the minimum cost. A heuristic based on adaptive demand grouping (ADG) is also proposed to solve the problem time-efficiently. Extensive simulations confirmed the performance of our proposals.

Dual-Stage Planning for Elastic Optical Networks Integrating Machine-Learning-Assisted QoT Estimation

Following the emergence of Elastic Optical Networks (EONs), Machine Learning (ML) has been intensively investigated as a promising methodology to address complex network management tasks, including, e.g., Quality of Transmission (QoT) estimation, fault management, and automatic adjustment of transmission parameters. Though several ML-based solutions for specific tasks have been proposed, how to integrate the outcome of such ML approaches inside Routing and Spectrum Assignment (RSA) models (which address the fundamental planning problem in EONs) is still an open research problem. In this study, we propose a dual-stage iterative RSA optimization framework that incorporates the QoT estimations provided by a ML regressor, used to define lightpaths’ reach constraints, into a Mixed Integer Linear Programming (MILP) formulation. The first stage minimizes the overall spectrum occupation, whereas the second stage maximizes the minimum inter-channel spacing between neighbor channels, without increasing the overall spectrum occupation obtained in the previous stage. During the second stage, additional interference constraints are generated, and these constraints are then added to the MILP at the next iteration round to exclude those lightpaths combinations that would exhibit unacceptable QoT. Our illustrative numerical results on realistic EON instances show that the proposed ML-assisted framework achieves spectrum occupation savings up to 52.4% (around 33% on average) in comparison to a traditional MILP-based RSA framework that uses conservative reach constraints based on margined analytical models.

Congestion aware ant colony optimisation algorithm for routing and spectrum assignment in flexi-grid optical burst switching networks

This paper presents an improved distributed ant colony optimisation algorithm for routing and spectrum assignment (RSA) on optical burst switched (OBS), flexible spectrum networks, with the spectrum continuity constraint imposed. The improved algorithm incorporates a distributed method for monitoring the congestion along the links and using this information to determine what route-spectrum combination should be selected to minimise the burst loss probability (BLP). Evaluations were performed on an optical burst switching simulator to determine the effect on BLP when a dynamic route congestion measure is considered as opposed to the use of the static route length measure. The evaluations, considering the effects of optical impairments, were made on different network topologies, with multiple spectrum widths provisioned on the network, and with different loads. The incorporation of congestion measures into the algorithm showed improvements in BLP over previous work in the range of 2–32% depending on the evaluation conditions.

Deep Reinforcement Learning-Based Routing and Spectrum Assignment of EONs by Exploiting GCN and RNN for Feature Extraction

Deep reinforcement learning (DRL) has been introduced to the routing and spectrum assignment (RSA) of elastic optical networks (EONs) where the RSA policies are learnt during the interaction of a DRL agent with the EON environment. Upon each new traffic arrival, the DRL agent senses the EON state, and makes the RSA decision accordingly. Therefore, the EON state feature extraction is essential for the performance. However, current approaches, mainly based on fully connected neural networks or convolutional neural networks, are unsatisfactory in EON state feature extraction due to the following two reasons. (1) The optical network topology information is not well considered. (2) The path-level features are highly related to the RSA decision, due to the spectrum continuity constraint, while path-level feature extraction has not been well exploited. To overcome the above shortcomings, in this paper, we propose to utilize the Graph Convolutional Neural Network (GCN) and the Recurrent Neural Network (RNN) for the feature extraction of the network topology, and the aggregation of link-level features to path-level features. By doing this, critical RSA-related information can be sensed by the DRL agent to make better actions. Simulation results demonstrate that our proposed method outperforms previous approaches.

A cost-efficient, load-balanced and fragmentation-aware approach for deployment of VNF service chains in Elastic Optical Networks

By emerging Network Function Virtualization (NFV), service providers use VNFs (Virtual Network Functions) and VNF service chain (VNF-SC) as an agile, scalable, and cost-efficient way for providing new network services. As traditional backbone networks fail to deal with the high volume and burstiness of traffic patterns generated by today popular services, using Elastic Optical Networks (EONs) for interconnecting data centers (inter-DC EONs) gets more attention. EONs are an ideal backbone for supporting NFV technology and transporting VNF-SC heterogeneous traffics. In this paper, we propose a solution named CELFA for the problem of VNF-SC deployment to minimize resource costs, balance resource utilization, and avoid spectrum fragmentation in EONs. At first, to find an optimal solution for saving computing and optical resources, we formulate the VNF placement problem as an Integer Quadratic Program (IQP). Then, we propose an efficient load-balanced and fragmentation-aware algorithm for the problem of Routing and Spectrum Assignment (RSA). The performance of the proposed strategy is evaluated based on resource balancing and blocking probability metrics. The simulation results verify that the proposed algorithm outperforms existing algorithms in terms of all important metrics.

Multi-Agent and Cooperative Deep Reinforcement Learning for Scalable Network Automation in Multi-Domain SD-EONs

The service provisioning in multi-domain software-defined elastic optical networks (SD-EONs) is an interesting but difficult problem to tackle, because the basic problem of lightpath provisioning, i.e., the routing and spectrum assignment (RSA), is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {NP}$ </tex-math></inline-formula> -hard, and each domain is owned and operated by a different carrier. Therefore, even though numerous RSA heuristics have been proposed, there does not exist a universal winner that can always achieve the lowest blocking probability in all the scenarios of a multi-domain SD-EON. This motivates us to revisit the inter-domain provisioning problem in this paper by leveraging deep reinforcement learning (DRL). Specifically, we propose DeepCoop, which is an inter-domain service framework that uses multiple cooperative DRL agents to achieve scalable network automation in a multi-domain SD-EON. DeepCoop employs a DRL agent in each domain to optimize intra-domain service provisioning, while a domain-level path computation element (PCE) is introduced to obtain the sequence of the domains to go through for each lightpath request. By sharing a restricted amount of information among each other, the DRL agents can make their decisions distributedly. To ensure scalability and universality, we design the action space of each DRL agent based on well-known RSA heuristics, and architect the agents based on the soft actor-critic (SAC) scenario. We run extensive simulations to evaluate DeepCoop, and the results show that DeepCoop can adapt to the dynamic environment in a multi-domain SD-EON to always select the best RSA heuristic for minimizing blocking probability, and it outperforms the existing algorithms on inter-domain provisioning in various scenarios. Moreover, we verify that the distributed training implemented in DeepCoop ensures its universality and scalability (i.e., its training and operation do not depend on the topology of the SD-EON).

Routing and spectrum assignment: A metaheuristic for hybrid ordering selection in elastic optical networks

Routing and spectrum assignment (RSA) problem, in elastic optical networks, searches for a route and a sufficient spectrum range to fit each network request. Solving these two problems simultaneously is a complex work. Therefore, they are usually assumed separately by either solving the routing firstly and then the spectrum assignment for this selected route or prioritizing some spectral ranges and then searching for a route that meets such spectrum requirements. The former strategy prioritizes the reduction on the network occupation by using short routes first, while the latter emphasizes load distribution and a more efficient spectrum organization. According to some aspects, such as network topology, number of alternative routes, traffic intensity and number of available slots, emphasizing either set of benefits results in superior advantage. This paper presents a comprehensive study about RSA ordering strategies and proposes a hybrid resource-allocation ordering approach to decide between either strategy. RSA ordering means performing spectrum assignment in function of routing or performing routing in function of some candidate spectrum ranges pre-computed by some SA algorithm criteria An efficient metaheuristic based on genetic algorithm is proposed to implement this strategy, named hybrid routing and spectrum assignment (HRSA). The proposed algorithm returns the most suitable RSA ordering choice for each source–destination pair, which provides near-lowest path-request blocking probability. Simulation results show that the RSA ordering returned by the proposed HRSA reduces the path-request blocking probability up to 35% in comparison to the classical RSA orderings.

Weight distributed spectrum allocation in flexible-grid optical networks

An Elastic or flexible-grid Optical Network (EON) is an emerging technology for efficient resource utilization. In this paper, a Weight distributed service provisioning (WDSP) algorithm for routing and spectrum assignment (RSA) based on a request for a connection is proposed. This novel technique for spectrum allocation based on the demands of the connection request reduces the blocking of connections due to improper spectrum assignment. The connection requests are classified into two categories based on demand: small and large connection requests. The smaller size connection requests are assigned to the lower index slots, and the larger size connection requests are assigned to the highest index slots. By implementing this approach, a relatively large number of connection requests can be handled via the availability of a large set of contiguous slots. The simulation results illustrate that the proposed algorithm is relatively more efficient due to enhanced blocking performance; effective spectrum efficiency can be achieved.

Hierarchical Routing and Resource Assignment in Spatial Channel Networks (SCNs): Oriented Toward the Massive SDM Era

In the past few decades, the architecture of optical networks has undergone significant evolution, from the earliest wavelength-division multiplexing (WDM) optical networks to elastic optical networks (EONs) and later to space-division multiplexing (SDM) EONs, to address the continuous growth of Internet traffic. By 2024, Pbps-level optical networks are expected, far exceeding the capacity limit of single-mode fibers. The massive SDM era is on the horizon. In this context, a newly designed architecture for optical networks called the spatial channel network (SCN) architecture, which achieves high cost efficiency by means of practical hierarchical optical cross-connects, has recently been proposed. However, the evolution of optical network architectures will simultaneously present challenges related to network optimization. For instance, with the evolution from WDM optical networks to EONs, the kernel network optimization problem was transformed from the routing and wavelength assignment (RWA) problem into the routing and spectrum assignment (RSA) problem due to the additionally introduced constraint of spectrum contiguity. Similarly, specially designed algorithms are also expected to be essential for addressing the network optimization problem in SCNs. In this paper, we define this new problem as the routing, spatial channel, and spectrum assignment (RSCSA) problem. We propose an integer linear programming (ILP) model and a heuristic algorithm to solve the RSCSA problem. We examine the performance of the proposed approaches via simulation experiments. The results show that both proposed approaches are effective in finding the optimal solutions or solutions close to the lower bounds. To the best of our knowledge, this is the first work to focus on the network optimization problem in SCNs.

Minimum-Cost Optical Amplifier Placement in Metro Networks

Emerging 5G services are revolutionizing the way operators manage and optimize their optical metro networks, and the metro network design process must be rethought accordingly. In particular, minimizing network cost is crucial to curb operators' investment. Taking advantage of relatively-short distances in metro networks, operators have the opportunity to optimize the placement of optical amplifiers (OAs) with the goal of minimizing amplifiers' cost (and hence decrease network cost) without significantly affecting the quality of transmitted optical signals. Minimizing OA cost translates not only in minimizing the cost of equipment (i.e., boosters, pre-amplifiers and inline amplifiers), but also in minimizing deployment and maintenance costs of active amplifier sites. In this article, we propose a heuristic algorithm for OA placement and for the Routing and Spectrum Assignment (RSA) in metro networks, with the objective of minimizing the total cost of OAs while guaranteeing sufficient optical signal-to-noise ratio (OSNR) of established lightpaths. In our approach, we consider different cost for the deployed OAs, according to their location and type, i.e., inline amplifiers (ILAs), boosters and pre-amplifiers, and compare our optimized placement against benchmark strategies where OAs are pre-deployed at network nodes and at a fixed distance one from the other along optical fiber links. We also evaluate the impact of different routing strategies on the total cost and utilized spectrum. Simulative results, performed over realistic metro network topologies, show that our strategy provides up to 47% OAs cost savings while satisfying minimum OSNR constraints.

Evaluation of Network Blocking Probability and Network Utilization Efficiency on Proposed Elastic Optical Network using RSA Algorithms

AbstractElastic optical networks (EONs) are new generation optical networks that provide flexible bandwidth and spectrum assignment characteristics to accommodate diverse demand range over traditional dense wavelength division multiplexing (DWDM)-based networks. While overcoming spectrum contiguity and continuity constraints, Routing and Spectrum Assignment (RSA) is a challenging task in EONs. In this article, we have proposed an EON network model. Using existing RSA techniques we have analyzed the performance of the proposed model on the basis of Network Blocking Probability (NBP) and Network Utilization Efficiency (NUE) under variable load conditions. It has been found to be working optimally even at a load of 200 Erlangs.

Impact of BER in fragmentation-aware routing and spectrum assignment in elastic optical networks

Routing and spectrum assignment (RSA) in elastic optical network (EON) is a challenging task as an inefficient RSA may result in spectral and spatial fragmentation, leading to under-utilization of spectrum and increased connection blocking. Previous fragmentation-aware RSA algorithms, based on the assumption of an ideal physical layer, are not suitable for the performance evaluation of practical networks, where quality of the optical signal degrades while travelling long distances through the optical fiber links. Therefore, while running a RSA algorithm, it is necessary to consider the impact of all noise components to evaluate receiver bit-error rate (BER) on the fly, so that each connection is setup only when the estimated BER remains below an acceptable limit. This problem, hitherto unexplored in EONs though more significant and complex as compared to the conventional optical networks, is taken into consideration in this paper while framing fragmentation-aware RSA algorithm. In particular, we present a novel BER-cum-fragmentation-aware RSA algorithm, by using online estimates of BER values while executing fragmentation-aware RSA. The simulation results show that awareness of BER increases the connection blocking, which can however be addressed favorably by increasing transmit power, as much as possible from the available transmitter modules. Finally, we present a histogram of number of established connections versus delay-bandwidth product of the connections, leading to an assessment of the proposed algorithm in respect of the ability to allow long-haul connections with large bandwidth requests.

The Recent Contributions of Routing and Spectrum Assignment Algorithms in Elastic Optical Network (EON)

The speed is an important aspect of the current generation of Internet users. Currently WDM based optical fiber technology provides a good solution, still it is insufficient for future growth of cloud data centers, social media applications, online gaming, high definition video streaming and broadcasting, etc. The Elastic Optical Networks (EONs) recently added an energetic research field in the fiber-optic network. The EON is next generation of network that can provide flexibly in spectrum, improved service provisioning and accommodate high bandwidth applications in the single channel. In WDM, the Routing and Wavelength Assignment (RWA) is problem, likewise the EON has Routing and Spectrum Assignment (RSA) problem. In RSA problem the frequency slots (FSs) should allocate in continuity and contiguous manner for every demand request. Many research articles are published over the years to find the new feasible solutions for the RSA problem. This paper presents the recent approaches based on routing and spectrum assignment problem and fragmentation schemes which examines and compare the performance, features, and complexity of the approaches. This paper provides merits and demerits of existing approaches in RSA problem and provides more information about the approaches it will help for feature research.

Grey Wolf Algorithm and Multi-Objective Model for the Manycast RSA Problem in EONs

Manycast routing and spectrum assignment (RSA) in elastic optical networks (EONs) has become a hot research field. In this paper, the mathematical model and high efficient algorithm to solve this challenging problem in EONs is investigated. First, a multi-objective optimization model, which minimizes network power consumption, the total occupied spectrum, and the maximum index of used frequency spectrum, is established. To handle this multi-objective optimization model, we integrate these three objectives into one by using a weighted sum strategy. To make the population distributed on the search domain uniformly, a uniform design method was developed. Based on this, an improved grey wolf optimization method (IGWO), which was inspired by PSO (Particle Swarm Optimization, PSO) and DE (Differential Evolution, DE), is proposed to solve the maximum model efficiently. To demonstrate high performance of the designed algorithm, a series of experiments are conducted using several different experimental scenes. Experimental results indicate that the proposed algorithm can obtain better results than the compared algorithm.

Recursive algorithm for selecting optimum routing tables to solve offline routing and spectrum assignment problem

Abstract The Routing and Spectrum Assignment (RSA) problem is NP-Hard so searching the entire problem space is not applicable. Many decomposition algorithms rely on reducing the search space in the routing space and applying heuristics algorithm in the spectrum assignment sub-problem. This is not necessarily a right solution as the ignored routing tables may lead to a better solution when they are used later as input to the Spectrum Assignment sub-problem. In this paper, we develop a new recursive decomposition approach for the RSA problem in optical networks. At the core of our approach is a new recursive branch and-bound procedure for carrying out an exhaustive search of the routing space in a scalable manner. This recursion effectively decouples the routing from the spectrum assignment part of the problem. Sequential generation of the full set of routing tables requires huge memory and very large processing time. Alternatively, our approach deploys multi-core architectures to generate the routing tables in parallel using OpenMP. Experimental results indicate that our recursive algorithm is quite efficient in searching the entire routing space for topologies representing large-scale wide area networks. Importantly, the decomposition may be more generally applied to any network design problem whose solution involves a search over both a routing and a resource allocation space. The main contributions for this paper are that we are able to generate all the search space in parallel in less than 1 min for 32-nodes network. Secondly, we are able to investigate all the routing tables, eliminate most of the search space, and select the promising routing tables that are proven to lead to a better solution in the Spectrum Assignment Sub-Problem

Spectrum defragmentation algorithms in elastic optical networks

Fragmentation in Elastic Optical Networks is an issue caused by isolated, non-aligned, and non-contiguous frequency slots that cannot be used to allocate new connection requests to the network, due to the optical layer restrictions imposed to the Routing and Spectrum Assignment (RSA) algorithms. To deal with this issue, several studies on Spectrum Defragmentation have already been presented. In this work we present an analysis of the different Defragmentation Algorithms in the literature, at the same time we compare the performance of those based on sequential approaches in terms of Blocking Probability, Entropy and Fragmentation Ratio. We also propose a Defragmentation Algorithm based on a Proactive-Reactive approach. Experimental results have shown the conditions under which a Proactive approach can outperform the Reactive ones, and when the Reactive approaches are a better option. Results also showed that our proposed Algorithm is a promissory solution to deal with the Fragmentation problem, because it outperforms the rest of the Sequential Defragmentation Algorithms considered in this study.

A routing and spectrum assignment algorithm in elastic optical network based on minimizing contiguity reduction

Elastic optical network, due to its flexibility of spectrum assignment, has been the most promising technology in the next generation of backbone transport network. Routing and spectrum assignment (RSA) is one of its key issues. Several RSA algorithms have been proposed in recent years. In the spectrum assignment process, the more contiguous the idle frequency slices, the more likely it is to find available spectrum blocks for connection requests. Therefore, in this paper, we first give the concept of link/path contiguity reduction (LCR/PCR) to express the change of spectrum contiguity on a link/path after a spectrum block is used. Then, we propose a RSA algorithm named Min-PCR-LCR, which always chooses a spectrum block with the minimum path contiguity reduction to a connection request, and if there is a tie, the one with the minimum sum of link contiguity reductions is finally chosen. Simulation results show that the Min-PCR-LCR algorithm can get lower blocking probability than the existing well-performed RSA algorithms.

Performance analysis of elastic optical networks (EONs) switches under unicast traffic

Bandwidth variable-wavelength cross connect switches (BV-WXCs) play an important role in the routing and spectrum assignment (RSA) problem in elastic optical networks (EONs). These switches, whose structure has been derived from micro electro mechanical systems technology, aim to switch the incoming traffic flexibly such that the existing resources are not wasted and more requests can be served. There exist different quality of service parameters which have to be provided in EON, such as delay, loss, jitter, and so forth. Among these parameters, blocking probability (BP) has been always taken into account in EON and several researches have been done to address it. The main objective of most RSA algorithms is to alleviate BP of requests through efficient usage of resources, such as frequency slots and BV-WXC ports. To show the importance of BV-WXC in routing and resource assignment in EON, its performance is analyzed in this paper. The BP of the BV-WXC is evaluated by means of an analytical model under unicast traffic and the obtained results are compared to the blocking probability results acquired from simulations for different number of input/output ports and available resources. The obtained results show that the analytical model has a good agreement with the simulation results.