With tremendous growth in bandwidth demand, energy consumption problem in elastic optical networks (EONs) becomes a hot topic with wide concern. The sliceable bandwidth-variable transponder in EON, which can transmit/...
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With tremendous growth in bandwidth demand, energy consumption problem in elastic optical networks (EONs) becomes a hot topic with wide concern. The sliceable bandwidth-variable transponder in EON, which can transmit/receive multiple optical flows, was recently proposed to improve a transponder's flexibility and save energy. In this paper, energy-efficient routing, modulation and spectrum allocation (EE-RMSA) in EONs with sliceable bandwidth-variable transponder is studied. To decrease the energy consumption, we develop a Mixed Integer Linear Programming (MILP) model with corresponding EE-RMSA algorithm for EONs. The MILP model jointly considers the modulation format and optical grooming in the process of routing and spectrumallocation with the objective of minimizing the energy consumption. With the help of genetic operators, the EE-RMSA algorithm iteratively optimizes the feasible routing path, modulation format and spectrum resources solutions by explore the whole search space. In order to save energy, the optical-layer grooming strategy is designed to transmit the lightpath requests. Finally, simulation results verify that the proposed scheme is able to reduce the energy consumption of the network while maintaining the blocking probability (BP) performance compare with the existing First-Fit-KSP algorithm, Iterative Flipping algorithm and EAMGSP algorithm especially in large network topology. Our results also demonstrate that the proposed EE-RMSA algorithm achieves almost the same performance as MILP on an 8-node network. (C) 2017 Elsevier Inc. All rights reserved.
The paper presents a novel method based on the standard tabu search (TS) approach, dedicated to solve the routing, modulation and spectrum allocation (RMSA) problem in elastic optical networks (EONs). The considered f...
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The paper presents a novel method based on the standard tabu search (TS) approach, dedicated to solve the routing, modulation and spectrum allocation (RMSA) problem in elastic optical networks (EONs). The considered formulation of the RMSA problem covers simultaneously unicast (one-to-one) and anycast (one-to-one-of-many) traffic demands. This is a very important issue taking into account the fact that anycasting gains more and more importance in contemporary Internet due the growing popularity of services like cloud computing, content delivery networks, and video streaming. In this paper, we formulate RMSA as an integer linear programming (ILP) problem and we study four different objective functions, which are related to, respectively, cost, power consumption, maximum and average spectrum usage. We evaluate the performance of our TS method based on the comparison with both optimal results yielded by the CPLEX solver and the results obtained by reference heuristic algorithms proposed in the literature. Moreover, we evaluate benefits of the use of anycasting in EONs. The performed simulation experiments demonstrate that the proposed algorithm outperforms other reference methods. What is more, we show that the anycast transmission can provide significant savings compared to the typical unicast transmission. (C) 2015 Elsevier B.V. All rights reserved.
To improve the network scalability, a large elastic optical network is typically segmented into multiple autonomous domains, where each domain possesses high autonomy and privacy. This architecture is referred to as t...
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To improve the network scalability, a large elastic optical network is typically segmented into multiple autonomous domains, where each domain possesses high autonomy and privacy. This architecture is referred to as the multi-domain elastic optical network (MDEON). In the MDEON, the routing, modulation, and spectrumallocation (RMSA) for the inter-domain service requests are challenging. As a result, deep reinforcement learning (DRL) has been introduced recently where the RMSA policies are learned during the interaction of the DRL agents with the MDEON environment. Due to the autonomy of each domain in the MDEON, joint RMSA is essential to improve the overall performance. To realize the joint RMSA, we propose a hierarchical reinforcement learning (HRL) framework which consists of a high-level DRL module and multiple low-level DRL modules (one for each domain), with the collaboration of DRL modules. More specifically, for the inter-domain service requests, the high-level module obtains some abstracted information from the low-level DRL modules and generates the inter-domain RMSA decision for the low-level modules. Then the low-level DRL module gives the intra-domain RMSA decision and feeds back to the high-level module. The proposed HRL framework preserves the autonomy of each single domain while delivering effective overall network performance through the cooperation of the high-level and low-level DRL modules. Simulation results demonstrate that our proposed method outperforms previous approaches.
There is a need of the networks which can accommodate the requirements of high data rate with low blocking probability. Optical networks are used most commonly as the backbone. They support high data rates, but need t...
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ISBN:
(纸本)9781538679029
There is a need of the networks which can accommodate the requirements of high data rate with low blocking probability. Optical networks are used most commonly as the backbone. They support high data rates, but need to be provisioned with low blocking probability. Due to the use of traditional Wavelength Division Multiplexing (WDM), optical network are not that much bandwidth efficient as they could be. Elastic Optical Network (EON) configuration can be used to resolve this problem if Routing and spectrum Assignment (RSA) problem is done efficiently. We are addressing the routing problem through an artificial intelligence-based algorithm. In earlier works, several optimization methods have been used for path-finding in the networks. Some of these studies has problem in terms of higher time complexity and incompleteness, due to which there is no guaranteed optimal solution. In this paper, we are using A* algorithm for routing, which is known for its best-estimated cost of a path from a source to a destination. To the best of our knowledge, this is the first attempt to study the use of A* algorithm in routing and spectrumallocation in the EON. We have modified this algorithm in a way such that, while reconstructing the path, we simultaneously check how many contiguous and continuous slots are available. So, while allocating the spectrum if the required slots are not available at that instant, we can either block or re-route the path. It saves time and decreases the blocking probability. Modified A* algorithm is expected to solve the problem only if initial seed values of the cost of the paths are not over estimated beyond a threshold. For the spectrum assignment, after evaluation of available slots while reconstructing the path, whichever slots are contiguously and continuously available, are allocated using different modulation formats. We have studied the algorithm for different network topologies, to check its validity and compared the results with the previously
For cost-effective transport services, we need to efficiently utilize spectrum resources in optical networks. Currently, we need to carefully design optical resources including routing, modulation format and slot assi...
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ISBN:
(纸本)9781665481557
For cost-effective transport services, we need to efficiently utilize spectrum resources in optical networks. Currently, we need to carefully design optical resources including routing, modulation format and slot assignment in order to improve of the efficiency of optical resources. In this paper, we propose a routing, modulation, and spectrumallocation (RMSA) method in optical networks in consideration of transmission quality and transmission parameters by GNPy. In the proposed method, the spectrum utilization of the optical network can be improved by selecting the route with the smaller spectrum slot number when searching for the route for optical path. We take into account the change of the required generalized signal-to-noise ratio (GSNR) and frequency bandwidth of the optical path due to the change of the modulation mode. Here, we utilize GNPy to estimate the transmission quality. In this way, we can obtain more realistic and highly efficient optical path design in consideration of requirements in operational optical networks. We evaluate the performance of the proposed method for optical networks in a simulation. In numerical examples, we show that our proposed method improves the efficiency of spectrum utilization. It also shows the effect of parameters on the performance and calculation time of the proposed method.
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