A RMSCA algorithm for space division multiplexing elastic optical networks with core switching

Auxiliary graph-based energy-efficient routing resource allocation algorithm in space division multiplexing elastic optical networks

A machine learning method for green energy saving in space division multiplexing elastic optical networks

A non-backtracking spectrum allocation algorithm in a large [formula omitted] under dynamic traffic grooming

Novel fragmentation-aware algorithms in space Division Multiplexing Elastic Optical Networks

Over the past decade, we have seen a substantial amount of research on elastic optical networks (EONs), showing great promise towards dramatically improving transmission capacity compared to traditional optical networks. The main concern is that the achievable transmission capacity for a given fiber will reach the physical limit soon in the case of single-core fibers. Multi-core fiber (MCF) has been proposed as an innovative alternative which is based on the concept of space-division multiplexing (SDM). We endeavour to work on a Crosstalk Elimination and Spectrum Defragmentation based Routing and Spectrum Allocation (CESD-RSA) algorithm has been proposed which is based on a predefined metric, which measures the spectrum status in the SDM-EONs. Past research has indicated the dependence of crosstalk effect in MCF on the assignment of signals in the context of the spectrum and the core. This specifically entails a study of the Routing, Core and Spectrum allocation in a flexi-grid multicore network. In the end we shall endeavour to evaluate the proposed method using computer simulations.KeywordsWDMSDMEONMCFRSASF

The evolution of generational networks has stimulated the growth of delay-sensitive traffic, destabilizing the already incapacitated existing network infrastructure. Due to time-constrained resource requirements of delay-sensitive traffic, managing end-to-end delays becomes very challenging with limited resources. Therefore, we introduce space-division-multiplexing-elastic-optical-networks (SDM-EONs) and point-to-multi-point (P2MP) transfer mechanisms to increase capacity transmission rates. However, managing resource allocation during SDM-EONs-P2MP transfer requires a multi-constraint optimization (MCO) approach to significantly overcome the severe impact of crosstalk (XT) during a joint optimization of multicast traffic in multi-core fibers (MCFs). To solve this problem, we propose a deadline-aware resource allocation algorithm (PRAA) based on mixed-integer linear programming (MILP) and a heuristic approach while leveraging a delay constraint to support a joint optimization of multicast traffic. During routing, modulation, spectrum, and core allocation (RMSCA), PRAA implements MCO to allocate MCFs to reduce congestion rates by improving sending rates. We simulate experimental results and compare them with two state-of-art algorithms: DaRTree-RMSCA and DST-OM-RMSCA, to evaluate PRAA’s performance during RMSCA. The output results highlight that PRAA-RMSCA outperforms DaRTree-RMSCA and DST-OM-RMSCA during peak load of fluctuating delay-sensitive traffic.

The explosive growth of data with the requirements of high reliability and low latency has posed huge challenges to the vehicular networks. One potential solution is to deploy the fog computing servers geographically closer to the vehicles to serve the vehicle-based applications in real time. However, due to the constraint of caching storage space as well as lack of tractable access mode selection and spectrum allocation algorithm, it is very challenging to balance the network transmission performance and fronthaul savings. In this paper, we investigate the joint optimization problem of access mode selection and spectrum allocation in fog computing-based vehicular networks. To solve the problem in an efficient way, the original high complexity optimization problem is divided into two subproblems. Wherein, the vehicle access mode selection problem is solved by Q-learning-based algorithm by considering spectrum allocation profiles and the fronthaul link cost. The randomly vehicular network topologies are modeled as CoX processes and the closed-form payoff expressions are derived by the stochastic geometry tools. On the other hand, the optimal spectrum allocation indicator value can be finally obtained via convex optimization. The analytical results for the proposed algorithm as well as the traditional baseline approaches are evaluated with different weight factors, which verify our theoretical analysis and confirm the proposed approach can achieve significant performance gains.

Study and Analysis of Routing and Spectrum Allocation (RSA) and Routing, Modulation and Spectrum Allocation (RMSA) Algorithms in Elastic Optical Networks (EONs)

Space-division multiplexing elastic optical networks (SDM-EONs) will play an important role in addressing the increasing Internet traffic, thanks to their spectrum utilization flexibility and superior capacity. However, besides traditional physical layer impairments (PLIs), newly introduced crosstalk (XT) coupled with the unpredictability of future services makes transmission quality assurance more challenging. Therefore, it is urgent to design more intelligent and effective resource assignment (RA) algorithms in SDM-EONs. The rise of artificial intelligence provides a clear solution to such problems. This paper proposes a novel RA scheme based on dynamic unsupervised fuzzy clustering considering both XT and PLIs. All resource combinations meeting services' transmission needs will be found to form an available resources set. If the sample scale is relatively large, we will exploit fuzzy C-means clustering for higher accuracy. To reduce the costs and complexity of clustering and also obtain better clustering results, a direct clustering method will be used for a small sample scale. The resource combination most suitable for the services' transmission needs will be assigned to different levels of services. Simulation results disclose that the cluster centers present a regional distribution which is consistent with resource occupation, and it can also effectively reduce blocking probability by an average of 59.68% while greatly improving resource utilization by 12.5% on average in the measured network load range.

Cognitive radio technology is considered to be an effective solution to improve the spectrum utilization efficiency. The dynamic spectrum allocation algorithm assigns the available spectrum to the secondary users. In this paper, we propose a spectrum allocation algorithm based on the graph-coloring theory. Unlike some already existed allocation algorithm, our algorithm considers the bandwidth matching between secondary users' bandwidth requirements and the available channels. The interference avoidance to the primary users is also taken into account. Our objective is to optimize the total spectrum reward under the premise of fully protecting the primary users. Simulation results prove that this algorithm can improve the total spectrum reward, the satisfaction rate of secondary users, the protection rate of primary users, and the spectrum allocation efficiency.

In order to solve the problem of the multi-objective spectrum allocation on the joint optimization of maximal network utility and fairness of users in cognitive radio network, based on quantum bee colony theory and membrane computing, a novel multi-objective discrete combinatorial optimization algorithm, named membrane-inspired quantum bee optimization, is proposed. The global optimal solution of single objective can be searched in the elementary membranes, and Pareto front solutions which take account of network utility and fairness, can be obtained from skin membrane with the proposed method. The multi-objective optimization algorithm, which can solve both single objective and multi-objective optimization problems at the same time, is designed by the communication rules between membranes, the cooperative evolution of foraging behavior based on quantum state, and non-dominated sorting. Compared with classical color-sensitive graph coloring algorithm, genetic algorithm, quantum genetic algorithm, and particle swarm optimization under different objective functions, the proposed spectrum allocation method can search the global optimal solution of single objective as shown by the simulation results, and it is superior to classical spectrum allocation algorithms and existing intelligence spectrum allocation methods. The optimal Pareto front solutions of multi-objective spectrum allocation are also obtained.

We focus on dynamic crosstalk-aware lightpath provisioning in translucent space-division multiplexing (SDM) elastic optical networks, in which spectral super-channels are carried over multi-core fibers (MCFs), distance-adaptive transmission is considered, and back-to-back (B2B) signal regeneration is applied. We aim at establishing lightpaths for connection requests in such a way that the inter-core crosstalk (XT) impairment affecting the quality of lightpaths in the network does not exceed allowable threshold levels. To this end, we propose and analyze a method that is based on per-core static/worst-case XT estimation. This method is implemented in a dynamic XT-aware routing, spatial mode, and spectrum allocation (RSSA) algorithm, which is used to solve the lightpath provisioning problem in an efficient way, such that the bandwidth blocking probability is minimized. The performed extensive simulation experiments in realistic network scenarios show a significant impact of this method on network performance and motivate for its application instead of commonly used per-fiber worst-case XT estimation.

We focus on a crosstalk-aware lightpath planning problem in a spectrally and spatially flexible optical network (SS-FON) in which spectral super-channels (SChs) are carried over multi-core fibers (MCFs) and distance-adaptive transmission is considered. The problem concerns establishing lightpath connections for a set of traffic demands in such a way that the inter-core crosstalk (XT) impairment affecting the quality of transmitted signals does not exceed allowable threshold levels. The aim of this work is to compare the methods ensuring required XT levels. In particular, we analyze two basic methods: a) based on worst-case XT and b) using precise XT estimation. Both methods are implemented in a XT-aware routing, spatial mode, and spectrum allocation (RSSA) algorithm, which is applied to solve the planning problem. The obtained numerical results show the impact of considered XT estimation methods on network performance.

Allowing Secondary Users (SUs) to access the licensed spectrum without causing harmful interference to the Primary Users (PUs) is crucial in enabling the Cognitive Radio (CR) technology. In order to increase the utilization of the spectrum bands, we propose a Dynamic Spectrum Allocation (DSA) algorithm that integrates both interweave and underlay spectrum access schemes. The proposed algorithm will jointly take into account the geographical locations of the nodes, the correlated shadow fading, the interference between the primary and the secondary networks, the interference between SUs that are transmitting on the same channel, and the communications activity of the users. Moreover, a suboptimal heuristic DSA algorithm that jointly takes into consideration all of the aforementioned issues, while requiring low computational- and time-costs, is developed. Simulation results show that the proposed algorithm provides a good success rate and outperforms classical spectrum allocation algorithms.

In this work, routing and spectrum allocation (RSA) algorithms together with network coding (NC) are proposed for elastic optical networks. NC has been used in optical networks for protection against link failures and also in multicasting to improve spectral efficiency. In this work, NC is used to protect confidential connections against eavesdropping attacks. The confidential signals are XOR-ed with other signals at different nodes in their path while transmitted through the network. These signals can be combined either at the source node and/or at intermediate nodes. To implement NC for confidential connections, a set of constraints for the NC problem in addition to the constraints of the RSA problem are incorporated to the algorithms. The combination of signals through network coding significantly increases the security of confidential connections, since an eavesdropper will receive a combination of signals from different connections, making it extremely difficult for the confidential signal to be decrypted. A number of RSA strategies are examined in terms of confidentiality, spectrum utilization, and blocking probability. Performance results demonstrate that network coding provides an additional layer of security for confidential connections with only a small increase in the spectrum usage.