Improved Routing and Spectrum Assignment formulations for optical OFDM networks

Recently proposed Elastic Optical Networks (EON) provide optimized bandwidth utilization through a better fitting match between connection bandwidth requirements and spectrum occupation. In an EON, lightpaths are established using a Routing and Spectrum Assignment (RSA) algorithm. Nonetheless, a novel problem arises in EON, known as the fragmentation problem. Indeed, as connections are dynamically established and terminated, spectrum may become fragmented and jeopardize the establishment of future connections. In this situation, the contiguity and continuity constraints in the RSA problem are harder to be guaranteed and therefore connections' blocking probability increases. In this paper we present and analyse some fragmentation-aware RSA algorithms currently found in the literature and analyse their performance when implemented alone or in tandem.

Elastic 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.

With the ever increasing demand for optical networks, many decisions about network planning are becoming increasingly challenging. One of these crucial decisions is the network provisioning, known as the Routing and Spectrum Assignment (RSA) problem. What makes this problem practically challenging to service providers is the significant difference in size between the realistic problem instances and the instances that can be solved using the existing algorithms. Motivated to solve larger problem sizes, we propose a decomposition model and a nested column generation algorithm for the RSA problem. Compared to recent studies, the proposed algorithm produces higher-quality solutions for larger data instances quite efficiently. Furthermore, it provides a measure of the solution accuracy, i.e., an upper bound on the maximum distance of the solution obtained compared to the optimal solution.

The routing and spectrum assignment (RSA) is one of the key problems in flexible optical networks. When in a gridless fashion, the shortest-path RSA algorithms have exponential computational complexity, and are thus not suitable to be employed in real networks. On the other hand, today most optical components cannot support fully gridless tunability, which also limits the application of gridless RSA schemes. In this paper, we propose a novel grid-based spectrum-scan routing (SSR) scheme in flexible optical networks. The SSR scheme achieves optimal routing with a polynomial computational complexity. Compared with other RSA schemes, SSR has shorter computation time, lower blocking probability, and higher resource utilization.

This paper presents the features of an advanced software simulator for the Elastic Optical Network (EON) - Complex Elastic Optical Networks Simulator (CEONS). The CEONS is as a test environment for solving three main issues addressed in EON: Routing and Spectrum Assignment (RSA) problems; Routing, Modulation and Spectrum Assignment (RMSA) problems, and addressing regenerator placement problem. With a universal API, users can design their own algorithms in any programming language and implement them in the simulator as plugins to solve the problems mentioned above. The ease and versatility of implementing the algorithms allows usage of the CEONS in advanced problems and for the introduction of EON technology.

Elastic Optical Networks has drawn a lot of attention in the past few years because of its ability to transmit different data rates increasing or decreasing the optical spectrum according to the necessary demand, ensuring a high spectral efficiency. However, establishing and tearing down different connections ends up segmenting the spectrum in several small fragments, making it impossible for new requests to be attended. Besides, another recurrent problem in elastic optical networks, called routing and spectrum assignment (RSA), aims to find a path and assign to it a contiguous number of spectrum slots using the smallest possible amount of spectral resources. The main purpose of this paper is to propose an algorithm to minimize the spectrum fragmentation problem in Elastic Optical Networks by using RSA algorithms. Results obtained through simulations indicates that the proposed algorithm outperforms in terms of blocking probability and spectrum fragmentation ratio the other existing ones.

<p>Upcoming metro networks are required to transport huge and highly dynamic data traffic whilst resulting cost and power efficient. Thus, novel metro network architectures are being investigated to increase the transport capacity (e.g., leveraging dense photonic integrated technologies), and provide effective SDN-based network programmability. The EU-H2020 PASSION project targets such a metro network design exploiting: i) programmable, modular, cost-efficient and low-power sliceable bandwidth variable transceivers (S-BVTs) formed by a pool of vertical cavity surface emitting lasers (VCSEL) and Coherent Receivers (CO-Rx); ii) hierarchical switching nodes in a flexi-grid network to attain all-optical transport and aggregation. The SDN-based control functions considers the features of the adopted PASSION transport layer. First, we detail the defined interfaces (APIs) for the automatic transport network programmability. Such (RESTful-based) interfaces are validated within the ADRENALINE testbed. Next, upon dynamic connection requests with heterogenous data rate demands, an on-line Routing and Spectrum Assignment (RSA) algorithm based on a modified K shortest path (K-SP) is proposed. To accommodate a connection request, a set of optical flows having their own S-BVT VCSEL and CO-Rx devices at the endpoints and path's frequency slots are selected and configured. Each optical flow is subject to the spectral continuity and contiguity constraints. The performance of the RSA algorithm for several traffic loads is experimentally evaluated through different figures of merit (e.g., blocked bandwidth ratio, average used of S-BVT devices, etc.) and diverse RSA's K-SP settings.</p>

The emergence of Elastic Optical Networks (EON) has brought new conceptions in the operations of optical networks, improving its flexibility and efficiency. The routing and spectrum assignment (RSA) is one of the key problems in EON which deals with resource allocation. In order to reduce the bandwidth blocking ratio on the network, recently, Auxiliary-Graph-Model approaches propose spectrum reservation schemes to reduce the network exhaustion probability. However, these approaches do not take into account the energy expenditure caused by these schemes. In this work, we use an auxiliary graph model to design a new RSA heuristic algorithm to improve the network energy consumption without losses in bandwidth blocking rate. Numerical results show that our proposal may provide a reduction of up to 54% in the bandwidth blocking ratio and achieve a gain of up to 51% in energy efficiency compared to literature.

In this paper, we propose a defragmentation based load balancing technique for efficiently undertaking routing and spectrum assignment (RSA), and minimizing the fragmentation problem in elastic optical network (EON). In EON, after connections terminate, they leave behind spectrum gaps which con not be re-assigned. To counter this condition, in Defragmentation based Load Balancing Routing and Spectrum Assignment (DLBRSA), the existing connections are reconfigured. At the time of defragmentation, for route selection, among the available shortest routes, the route with the largest chunk of contiguous frequency slots (FSs) is chosen. DLBRSA has three advantages over the conventional Shortest Path RSA (SPRSA) technique: (1) defragmentation increases the network resource utilization, (2) load balancing on the basis of the largest available FS chunk along the route ensures uniform spectrum allocation, and (3) defragmentation ensures that the route length of the request is minimum. The performance of the DLBRSA is analyzed in reference to a benchmark, SPRSA strategy. The comparison is done on the metrics of bandwidth rejected, and the percentage of total network capacity allocated for the EON and NSFNET network topologies. These two parameters are evaluated under the two scenarios of increase in: network load (in Erlang) and number of FSs demand. Simulation results show lower bandwidth blocking probability of DLBRSA from 6.53% to 8.96% under the different load conditions. In addition, decrease of network resource utilization is observed, when we compare the proposed DLBRSA with SPRSA the existing strategy.

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.

De-multiplexing the required spectrum in a traffic demand into multiple non-adjacent granular spectrums for dynamic traffic grooming in EON

We consider the static routing and spectrum assignment (RSA) in co-existing fixed/flex grid optical networks. Integer Linear Programming (ILP) formulations are presented to minimize the utilized spectrum, and several heuristic algorithms are proposed and simulated.

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.

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).

With flexibility in optical layer, elastic optical network (EON) has been considered as a competitive candidate to architect next-generation backbone networks. Routing and spectrum assignment (RSA) is a key problem for the service provisioning in EONs. The RSA problem is NP-hard even in elastic optical rings. Numerous heuristics have been proposed, and they can generally be categorized into two types: Route-First (RF) and Spectrum-First (SF). Although most previous work demonstrated by numerical simulations that the SF algorithms always outperform the RF ones, there is a lack of theoretical analysis on the reasons causing the performance difference between the two types of RSA algorithms. In this paper, we aim at proposing a unified theoretical framework for the performance analysis of RSA algorithms by leveraging conflict graphs, which offers a new perception on the optimality of RSA algorithms. To validate the proposed framework, we apply it in elastic optical rings (with cycle topology), and theoretically analyze the number of edges of the conflict graphs for RF and SF algorithms. Different from the literature, we obtain an interesting observation that neither the RF nor the SF can surpass the other in elastic optical rings under different traffic distributions, and their performances have a strong correlation to the edge count of their conflict graph. This observation provides a new perspective, i.e., conflict graph, to explore the property of RSA algorithms.