Routing And Spectrum Allocation Research Articles

Security enhanced routing and spectrum allocation against crosstalk attacks for confidential lightpath in elastic optical networks.

Elastic optical network (EON) is a critical transmission infrastructure for emerging new applications due to its spectral efficiency and flexibility. Nowadays, numerous confidential lightpaths (CLPs) are carried over EON to support security-sensitive users. However, they are vulnerable to crosstalk attacks at the optical layer, typically aimed at eavesdropping on the carried data or even disrupting connections. Due to the transparent nature of the optical signals, such attacks are difficult to detect and could last for a long time, resulting in data leakage even spreading throughout the network. This paper presents a novel routing and spectrum allocation (RSA) algorithm to protect CLPs from crosstalk attacks. We investigate intra-channel and inter-channel crosstalk attacks and develop a metric to quantify crosstalk leakage risks (CLRs). We first formulate an ILP model to plan CLPs with a minimum CLR. To solve the same problem for large-scale networks, we also propose a heuristic algorithm, i.e., crosstalk-attack-aware RSA. Results indicate that the proposed algorithm is capable of reducing CLR by 23%.

Optical Networks Automation Overview: A Survey

The increasing demand for data has driven the advancement of optical networks from traditional architectures to more flexible, dynamic and efficient solutions. This includes technologies like flexgrid reconfigurable optical add-drop multiplexers (ROADMs), variable bandwidth transponders (VBTs) providing different modulation, coding schemes and baud rates. These advancements have brought about new challenges that concerns to the routing and spectrum allocation (RSA), fragmented spectrum, need for rapid and efficient channel restoration, and operation and maintenance management of optical networks. To address these challenges, a dynamic and flexible network requires a highly advanced network operational system (OS) capable of efficiently managing and allocating network resources. It relies on network abstraction, sensors, actuators, and software-defined networking (SDN) to enable algorithms, management, control, and decision-making. Improving the sensing capabilities of the network is crucial. Modern hardware and sensor technology can help forecast fiber breaks, equipment failures, and other potential issues in advance, allowing for proactive actions to be taken. Machine learning (ML) methods have been proposed in the literature to enhance the accuracy of quality of transmission (QoT) estimation, mitigate nonlinearities and provide decisions. This reduces the need for conservative design margins, maximizes the capacity of optical network systems and reduces the investment in infrastructure. Failure management is a critical aspect of optical networks. Providing early-warning and proactive protection is essential. This includes detecting failures, localizing them, identifying the root causes, and estimating their magnitude. Quick response to failures is vital to maintaining network reliability

Performance of elastic optical network with limited slicers

A bandwidth blocking occurs due to bandwidth fragmentation in an elastic optical network, which arises from setting up and tearing down of lightpaths. An appropriate routing and spectrum allocation (RSA) approach can reduce this blocking. RSA performs under two allocation rules, which are continuous connection and consecutive slots. A slicing-and-stitching technology excludes the second rule by splitting an optical signal into spectrum components using slicers. Adopting slicers for all nodes is costly. This paper proposes EON with limited number of slicers. A set of nodes with slicers is determined. The pattern of slicers to achieve low bandwidth blocking is investigated.

A polyhedral study of a relaxation of the routing and spectrum allocation problem (Brief Announcement)

The routing and spectrum allocation (RSA) problem arises in the context of flexible grid optical networks, and consists in routing a set of demands through a network while simultaneously assigning a bandwidth to each demand, subject to non-overlapping constraints. One of the most effective integer programming formulations for RSA is the DR-AOV formulation, presented in a previous work. In this work we explore a relaxation of this formulation with a subset of variables from the original formulation, in order to identify valid inequalities that could be useful within a cutting-plane environment for tackling RSA. We present basic properties of this relaxed formulation, we identify several families of facet-inducing inequalities, and we show that they can be separated in polynomial time.

DYNAMIC MULTICAST SPECTRUM (RE) ALLOCATION USING SHARED BACKUP PATH IN ELASTIC OPTICAL NETWORKS

The routing and spectrum allocation (RSA) problem in elastic optical networks (EON) is to first find a path for a connection request and then allocate a frequency slot to it. Due to spectrum continuity and contiguity constraints, some available frequency slots cannot be allocated to connection requests. In this case, one solution is to reassign slot to some existing connections to accept new ones. However, most current reallocation schemes are proposed for unprotected connections. With the very high throughput of connections in EONs (Tb/s per link), the requirement for connection protection is becoming standard. In this paper, we examine a frequency slot reallocation approach for protected multicast connections. The proposed approach allows the reallocation of available and already used slots by temporarily using the protection resources. The experimental results show an average blocking probability of 10% lower than reallocation with hoptuning and the conventional MC-RSA algorithm.

Routing and Spectrum Allocation Using Elastic Optical Network.

With the rapid growth of cloud data, the internet of things, social media applications, high-end video streaming, etc WDM[3][3] is slowly becoming insufficient for all these future growths. The elastic optical network is slowly becoming the solution for these problems in the fiber-optic network. It can provide flexibility in the spectrum assignment and can even provide greater bandwidth. But with all these benefits come the problem of routing and spectrum allocation(RSA). In this problem, the frequency slots should be allocated to maintain continuity, contiguity, and nonoverlapping constraints. The paper explores the feasible way of solving the problem so that requests can be transferred through the network with minimum cost production.. algorithms that can solve RSA problem with static traffic demand. So, in this paper we incorporate a layered approach to de-sign efficiently integrated MC-RSA for serving multicast requests in EONs. This algorithm decomposes the physical structure of the graph into several layered auxiliary graphs according to the given multicast request . Then depending on the bandwidth we create a multicast light tree . It solves the routing and spectrum allocation problem by first finding the k-shortest path in the first available contiguous slots. However, the main goal is to allocate the connection request list that we have implemented only if there are available contiguous slots. We have tried to decode the algorithm and make it as efficient as possible.

Online routing and spectrum allocation in elastic optical networks based on dueling Deep Q-network

The study of online routing and spectrum allocation (RSA) problem has assumed increasing importance due to the exponential growth of dynamic traffic with uncertainty in elastic optical networks (EONs). This paper first formulates offline RSA as a mixed-integer linear program (MILP) with the consideration of the time attribute, and then models online RSA by a carefully designed Markov decision process (MDP), including the state, action and reward. To deal with such a complex dynamic optimization problem, a novel algorithm based on the classic deep reinforcement learning (DRL) framework, Deep Q-network (DQN), is developed. For further promoting the training efficiency, a dueling network architecture, an improved ɛ-greedy strategy and a series of parameter adjustments are applied. Simulation results demonstrate the effectiveness of the proposed algorithm and show its superiority in reducing the blocking probability compared with the state of the art, which further exhibits the potential of applying DRL to solve such complex real-time decision-making problems.

Routing and spectrum assignment employing long short-term memory technique for elastic optical networks

With the prevalence of some high bandwidth-demanding applications, such as cloud computing, traditional wavelength-division-multiplexing passive optical networks have difficulties in satisfying such growing bandwidth demands due to its limited allocation-flexibility and utilization-efficiency. Therefore, elastic optical networks (EONs). In order to realize the flexibility in EONs, sophisticated routing and spectrum allocation (RSA) algorithms areone of the keyenabling technologies. However, most of the previous RSA algorithms were proposed with invariant routing and spectrum allocation strategies, which ignored considering the time-varying characteristics of EONs due to the variable network architecture and service provisioning. And such time-varying characteristics can deteriorate the spectrum fragmentation and the service blocking performances of EONs, which stimulates the application of various machine-learning technologies in EONs. In this paper, a long short-term memory based routing and spectrum assignment (LSTM-RSA) algorithm is proposed for EONs. By employing the long short-term memory technique to sense the complex status of EONs (e.g. spectral usage on the selected paths), the proposed LSTM-RSA algorithm gradually learns successful strategies through accumulating operation experience in the process of interaction and obtains higher returns through enhanced operation, which helps improve the spectrum fragmentation and the service blocking performances in EONs. Simulation results show that the spectrum fragmentation rate and the blocking rate of the proposed LSTM-RSA algorithm are reduced by about 6% and 8.9%, respectively, when compared to the traditional shortest-path-routing first-fitting RSA algorithm.

A novel strategy to enhance the quality of service (QoS) for data center traffic in elastic optical networks

Abstract Elastic optical networks (EONs) offer tremendous benefits to deal with the exponential increase of the data center traffic. The granularity offered in spectrum allocation supports efficient management of available bandwidth and accommodates multiple traffic to be routed through common links. However, this brings the inherent challenges of routing and spectrum allocation (RSA) constraints. This becomes more complex for elastic optical data center networks (EODCNs), wherein multiple requests arrive at the same time, requiring identical or different bandwidths and each request may have the same or different destination and paths. Also, data requested by different users could be of varying importance levels. Under such a scenario, maintaining the quality of service (QoS) by minimizing the probability of traffic failure and bandwidth blocking is a major task for service providers. To address these problems, we propose an enhanced methodology using path prediction and link-state analysis for efficient allocation of frequency slots and reuse of bandwidth for data centers connected through EONs. Our proposed strategy intents to minimize the number of blocked requests due to non-availability of resources and reduce the failure probability. We introduce here the concept of connectivity degree and Kuhn-Munkres multi-objective optimization for spectrum allocation. We also evaluate the call request blocking probability varying the number of data centers and traffic load. The obtained results show that the proposed algorithm is highly effective in reducing the traffic failure and blocking probability for EODCNs.

Routing and Spectrum Allocation in Spectrum-Sliced Elastic Optical Path Networks: A Primal-Dual Framework

The recent decade has witnessed a tremendous growth of Internet traffic, which is expected to continue climbing for the foreseeable future. As a new paradigm, Spectrum-sliced Elastic Optical Path (SLICE) networks promise abundant (elastic) bandwidth to address the traffic explosion, while bearing other inherent advantages including enhanced signal quality and extended reachability. The fundamental problem in SLICE networks is to route each traffic demand along a lightpath with continuously and consecutively available sub-carriers, which is known as the Routing and Spectrum Allocation (RSA) problem. Given its NP-Hardness, the solutions to the RSA problem can be classified into two categories: optimal solutions using link-based, path-based, and channel-based Integer Linear Programming (ILP) models, which require extensive computational time; and sub-optimal heuristic and meta-heuristic algorithms, which have no guarantee on the solution quality. In this work, inspired by a channel-based ILP model, we propose a novel primal-dual framework to address the RSA problem, which can obtain a near-optimal solution with guaranteed per-instance closeness to the optimal solution.

E-S-RSM-RSA: A Novel Energy and Spectrum Efficient Regenerator Aware Multipath Based Survivable RSA in Offline EON

This paper proposes the design of energy and spectrum efficient, survivable Routing and Spectrum Allocation (RSA) scheme for regenerator aware Elastic Optical Network (EON) under static environment. Regenerator(s), after being placed at the intermediate site(s) of the EON, helps in reducing the spectrum required for a traffic demand associated with the connection request, at the expense of its huge power consumption. Any effort to ensure survivability also leads to an increase in power consumption due to provisioning redundancy in EON. All these factors imply a trade-off to exist in between total power consumption and spectrum utilization while the placement of regenerator(s) in EON plays a key role. A two step Integer Linear Programming based model has been formulated to design multipath based survivable RSA while optimizing spectrum requirement and energy consumption in regenerator aware offline EON jointly. E-S-RSM-RSA problem is an NP-hard one and hence intractable in nature. A Heuristic algorithm based on the Non-dominated Sorting Genetic Algorithm addressing this objective has been developed. The heuristic performance is compared with that of the ILP model in terms of spectrum utilization and energy consumption. Simulation results of the heuristic tested on two large-sized topologies also confirm the existing trade-off.

Penalty-method based impairment-aware performance of Elastic Optical Networks with spectrum conversion

In Elastic Optical Networks (EONs), fragmentation is a major bottleneck which affects the spectral efficiency significantly. Many defragmentation techniques have been proposed to tackle this issue and often, these schemes involve complex operations at the controllers and various sophisticated devices. Moreover, the amount of defragmentation achieved is also limited. In this regard, recently, spectrum conversion is gaining attention as a potential solution for the fragmentation issue in EONs. On the other hand, physical-layer impairments such as non-linear interference, amplified spontaneous emission (ASE) noise, beat noises due to coherent detection, etc. aggravate the fragmentation issue significantly. In this paper, we propose a novel impairment-aware routing and spectrum allocation (RSA) scheme with allowable spectrum conversion at intermediate nodes. We also propose a novel node architecture involving spectrum conversion with full switching flexibility. We first formulate a mixed integer linear program (MILP) optimization framework based on the proposed architecture in presence of impairments for a sample network and propose a penalty-method-based heuristic algorithm for realistic networks. Within our optimization framework, we ensure that a minimum end-to-end bit error rate (BER) for each demand is provisioned.

Performance of routing and spectrum allocation (RSA) algorithms for a last generation centralized optical network (SDON)

Introducción: Artículo producto de la investigación “Análisis comparativo del desempeño de algoritmos de asignación de espectro (RSA) de una red óptica distribuida a una red óptica centralizada limitadas en recursos”.

Link Congestion Aware Proactive Routing for Dynamic Traffic in Elastic Optical Networks

Fixed alternate routing is a potential routing scheme for routing and spectrum allocation (RSA) in elastic optical networks (EON) which has less complexity and time consumption compared to adaptive routing scheme. However, adaptive routing scheme efficiently reduces the amount of network bandwidth blocking probabilities (BBPs) which uses traffic engineered paths and tunes according to the current network status. In this paper, an algorithm for routing is proposed for dynamic traffic in EON which works iteratively to arrange the pre-computed fixed alternate routes offline to incorporate link loading. During the offline process, the pre-computed routes are arranged with an objective to reduce link congestion and diverts lightpaths to the under-utilized links. The proposed scheme merges the properties of fixed alternate routing and adaptive routing and is utilized for dynamic traffic in EON. It has been shown through simulation results that the proposed scheme efficiently improves the performance of RSA in EON and reduces the amount of BBPs compared to the fixed alternate routing and an existing constrained-lower-indexed-block (CLIB) based adaptive routing algorithm. The proposed LCA greatly reduces congestion over all links during dynamic network operation and lowers congestion spikes over some links which occues in the existing alternate routing scheme in different network scenarios.

Static routing and spectrum allocation method for design of low-power elastic optical networks with multifiber environments

This paper proposes a static Routing and Spectrum Allocation (RSA) method to design low-power elastic optical networks. In the elastic optical networks, data are transmitted via lightpaths, which are routes flexibly assigned frequency resources according to transmission distances and traffic demands. When designing the elastic optical networks with a static lightpath establishment policy, lightpaths are generally established so as to minimize frequency bandwidth allocated in order to save frequency resources necessary for constructing the networks. The proposed method focuses on not only this performance metric but also the power consumption of optical amplifiers. An optical amplifier in an optical fiber works when any of optical signals pass through it, and thus in multifiber environments where each link consists of multiple optical fibers, lightpaths should be collectively amplified from the view point of the power efficiency. Therefore, the relationship between these two performance metrics is a trade-off. The proposed method develops an Integer Linear Programming (ILP) formulation for the RSA problem taking the trade-off into account. Furthermore, the proposed method provides a heuristic algorithm that performs route selection and frequency spectrum allocation for reducing the total power consumption of optical amplifiers while saving necessary frequency resources. Through numerical experiments, we show the performance of the proposed method.

Overview on routing and resource allocation based machine learning in optical networks

For optical networks, routing and resource allocation which considerably determines the resource efficiency and network capacity is one of the most important works. It has been widely studied and many excellent algorithms have been developed. However, theoretical analysis shows that routing and resource allocation belongs to the Nondeterministic Polynomial Complete (NP-C) problem no matter in wavelength division multiplexing (WDM) optical networks, elastic optical networks (EONs), or space division multiplexing (SDM) optical networks. At presents, there doesn't exist a polynomial-time algorithm for routing and resource allocation. In recent years, machine learning which shows great advantages in solving complex problems has been widely concerned and researched. Using machine learning to conduct routing and resource allocation has aroused a great interest of researchers. This paper provides an overview on routing and resource allocation based on machine learning in optical networks. At first, we briefly introduce the routing and wavelength allocation (RWA) problem in WDM optical networks, the routing and spectrum allocation (RSA) problem in EONs, and the routing, core, spectrum allocation (RCSA) problem in SDM optical networks respectively. Commonly used machine learning techniques in optical networks are briefly elaborated. Then, the problems of quality of transmission (QoT) estimation, traffic estimation, and crosstalk prediction which can help to routing and resource allocation are also elaborated. The machine learning enabled RWA algorithms, RSA algorithms, and RCSA algorithms are elaborated, analyzed and compared in detail. In addition, the applications of machine learning in the QoT estimation, traffic estimation, and crosstalk prediction, etc., are also elaborated. Based on the existing research results, we present future research directions about how to use machine learning techniques to conduct routing and resource allocation in multidimensional time–space-frequency optical networks and satellite optical networks.

On solving the capacitated routing and spectrum allocation problem for flexgrid optical networks

The elastic optical networks, a.k.a., flexgrid networks, have flexible spectrum allocation. Such flexibility is possible due to the division of the spectrum into narrow frequency slots and the use of bandwidth-variable transponders. This enables the demand, in terms of band request, to be contiguously allocated starting from any available slot of the spectrum. In this context, the RSA (Routing and Spectrum Allocation) was proposed as an offline optimization problem of allocating the optical transmission resources in order to meet a predefined set of demands. This work deals with the capacitated version of the RSA problem. To solve it, we develop a multi-commodity flow formulation based on ILP (Integer Linear Programming) and a load balancing procedure that generate promising paths for an ILP formulation from the literature. Extensive computational experiments show that the proposed solution approaches are capable of obtaining high quality solutions on a variety of scenarios.

Elastic optical network with spectrum slicing for fragmented bandwidth allocation

Elastic Optical Networks (EONs) allow the channel spacing and the spectral width of an optical signal to be dynamically adjusted and hence have become an important paradigm in managing the heterogeneous bandwidth demands of optical backbone networks. The entire available optical spectrum is divided into some spectrum slots which define the smallest granularity of bandwidth and optical signals with variable bandwidths can occupy different number of such slots. The constraints imposed by the physical layer of an EON require that the slots occupied by an optical signal from source to destination have to be consecutive and contiguous in terms of their relative position in the optical spectrum. Furthermore, the same spectrum slots need to be reserved throughout the entire optical signal's path from its source to destination. The above constraints make the routing and spectrum allocation (RSA) in EONs very challenging because unavailability of enough spectrum slots that together equals the spectral width of the optical signal associated with an end-to-end request, will result in blocking of the request. Recent developments in the physical layer technologies have made all-optical ‘slicing’ of a request possible and make the request to be ‘fit’ into multiple non-consecutive spectral slots in an EON. But these all-optical ‘slicers’ employ complex technologies and can be very costly to employ. In this paper, we propose a spectrum allocation scheme for an EON node architecture with these ‘slicers’ and we also formulate a modified RSA scheme for EONs employing slicers, both as a mixed-integer linear programming (MILP) model and a heuristic algorithm. Our main aim is to analyze the tradeoff between the number of slicers that can be used per node versus the spectrum utilization and bandwidth blocking rate. The numerical results show that the proposed scheme with slicers can significantly improve bandwidth blocking rate, compared to the conventional scheme without slicer.

Invalid-Resource-Aware Spectrum Assignment for Advanced-Reservation Traffic in Elastic Optical Network

Elastic optical networks (EONs) can make service accommodation more flexible and precise by employing efficient routing and spectrum allocation (RSA) algorithms. In order to improve the efficiency of RSA algorithms, the advanced-reservation technique was introduced into designing RSA algorithms. However, few of these advanced-reservation-based RSA algorithms were focused on the unavailable spectrum resources in EONs. In this paper, we propose an Advanced-Reservation-based Invalid-Spectrum-Aware (AR-ISA) resource allocation algorithm to improve the networking performance and the resource alignment of EONs. By employing a new index, Invalid Spectrum Rate (ISR), to record the proportion of unavailable spectrum resources in EONs, the proposed AR-ISA algorithm set a network load threshold to trigger the postponement of an arriving service. Compared with the traditional slack-based AR mechanism, the proposed algorithm has more concerns about the current spectrum usage of the path designated by the service than the conflicts between AR services and other existing services. To further increase the networking performance, the proposed algorithm adopts defragmentation to increase the number of available spectrum resources when postponing a service. Theoretical analysis and simulation results show that the proposed AR-ISA algorithm has obvious effectiveness in reducing the service blocking rate and increasing the spectrum alignment rate.

Performance of routing and spectrum allocation approaches for multicast traffic in elastic optical networks

In this paper, we investigate routing and spectrum allocation (RSA) approaches in elastic optical networks for multicast traffic demands. We consider that a light-tree is constructed for each multicast traffic demand and spectrum is allocated to this light-tree using the first-fit spectrum allocation policy. We present an approximation based Steiner tree approach for RSA (STA-RSA) and compare it with the shortest path tree based routing and spectrum allocation (SPT-RSA) to show the benefits of STA-RSA over SPT-RSA. The numerical results show that the presented approach outperforms the traditional approaches in terms of bandwidth blocking probability.