A K-Hybrid Paths (K-HP) based Routing, modulation and spectrum assignment (RMSA) algorithm is proposed for Elastic Optical Networks. Simulation experiments are performed on two realistic network topologies to test the...
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ISBN:
(纸本)9798350340846;9798350340839
A K-Hybrid Paths (K-HP) based Routing, modulation and spectrum assignment (RMSA) algorithm is proposed for Elastic Optical Networks. Simulation experiments are performed on two realistic network topologies to test the algorithm's performance. Simulation results verify the proposed K-HP-RMSA algorithm offers lower connection blocking under low as well as high traffic load conditions when compared to benchmark K-Shortest Paths-RMSA and K-Disjoint Paths (K-DP)-RMSA algorithms. It also borrows the K-DP-RMSA algorithm's resilience without compromising spectrum efficiency.
Deep reinforcement learning (DRL) enables autonomic optical networking by allowing the network control and management systems to self-learn successful networking policies from operational experiences. This paper propo...
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Deep reinforcement learning (DRL) enables autonomic optical networking by allowing the network control and management systems to self-learn successful networking policies from operational experiences. This paper proposes a transfer learning approach for effective and scalable DRL in optical networks. We first present a modular DRL agent design to facilitate retrieving and transferring relevant knowledge between tasks requiring different dimensions of network state data. In particular, we partition network state data into common states, which contain generic information critical to multiple tasks (e.g., the spectrum utilization on fiber links), and task-specific states that are only used by a specific task (e.g., the utilization of virtual network functions). Separate neural network blocks are employed to process different state data. Based on the modular agent design, a multi-task learning (MTL) aided knowledge transferring scheme is proposed. The proposed scheme trains an MTL agent that can master multiple tasks simultaneously and thus enables to learn and transfer better-generalized knowledge across tasks. We perform case studies on the proposed transfer DRL approach considering two scenarios, namely, knowledge transferring between routing, modulation and spectrum assignment (RMSA) tasks for different networks and knowledge transferring from RMSA tasks to anycast service provisioning tasks. The DRL designs for RMSA and anycast service provisioning, including the state, action, and reward formulations and the training mechanisms, are also elaborated. Performance evaluations under both scenarios show that the proposed approach can effectively expedite the training processes of the target tasks and improve the ultimate service throughput.
Multicasting, as a main transmission mode for most of the current applications in elastic optical network, has attracted more and more research attention. In this paper, we investigate multicast scheduling model and s...
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Multicasting, as a main transmission mode for most of the current applications in elastic optical network, has attracted more and more research attention. In this paper, we investigate multicast scheduling model and solving algorithm. First, we model the multicast scheduling problem as a multi-objective optimization problem (MOP) by minimizing the bandwidth resources and maximizing the user service quality, and then, we propose a deep reinforcement learning assisted multi-objective algorithm for the model (DRL-MM), in which we design source node selection strategy, routing scheme, modulation scheme and spectrumassignment scheme for each multicast session. To identify the superiority of the proposed DRL-MM, we conduct the experiments and compare DRL-MM with an approximation based Steiner tree algorithm (STA-RSA) and a load-balancing routing tree-based algorithm (LD-RSA) through the experiments. The results show that DRL-MM outperforms STA-RSA and LD-RSA in terms of both bandwidth resource usage and user service quality.
The rapidly increasing data demand from current Internet services, such as cloud computing and high-quality video streaming, is raising the pressure on network operators to provide reliable high-speed connections whil...
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The rapidly increasing data demand from current Internet services, such as cloud computing and high-quality video streaming, is raising the pressure on network operators to provide reliable high-speed connections while keeping costs low. Without having to rely on the bandwidth expansion of optical waveguides or modulation level efficiency, one of the ways to increase spectral efficiency of a network is by the optimal utilization of already existing resources. For elastic optical networks (EON) this can be solved by the Routing, modulation and spectrum assignment (RMSA). However, another problem arises, due to the nature of computer network traffic, that makes it difficult to assess the longevity of physical components and to plan a network to serve a maximum amount of bandwidth demand for predetermined time span. To help alleviating these problems, this paper presents an investigation of a novel technique for resource planning and consume estimation based on simulations. First, traffic matrix prediction is made using recurrent neural networks (RNN), and then simulations are ran to estimate the consumed bandwidth for a point in time. For the traffic forecasting, real traffic data has been used to train the RNN, obtained from an anonymous dataset containing the traffic history of the ABILENE and GEANT networks from the years 2004-2005, which is a relatively recent available dataset. Furthermore, heuristic algorithms are proposed to apply the RMSA to the predicted traffic, in a quasi-optimal manner, in order to minimize the use of network resources for future traffic, by releasing available capacity in the existing optical fiber links in an incremental traffic approach. The results show that the proposed RNN models are indeed capable of predicting the traffic matrices a month ahead, with low mean squared error. RMSA simulations were also performed using heuristics in order to estimate the consumed bandwidth in the context of EON networks, revealing that, for the propos
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