Virtual Topology Reconfiguration Issues in Evolution of WDM Optical Networks

We consider the reconfiguration problem in multi-fiber WDM optical networks. In a real-time network as the traffic evolves with time: the virtual topology may not remain optimal for the evolving traffic, leading to a degradation of network performance. However, adapting the virtual topology to the changing traffic may lead to service disruption. This optimization problem hence captures the trade-off between network performance and number of reconfigurations applied to the virtual topology. The above problem is solved through a Mixed Integer Linear Programming formulation with a multivariate objective function, that captures both these parameters. However the problem is NP-hard and such an approach is unable to solve large problem instances in a reasonable time. In this paper, we also propose a simulated annealing based heuristic algorithm for solving problems of higher complexity. We compare the performance and the computation time of the MILP model and the heuristic algorithm considering different tests instances. Our results indicate that simulated annealing obtains results within 5% of the optimal solution, thus making it a viable approach in large scale networks

Load Balancing with Energy Storage Systems Based on Co-Simulation of Multiple Smart Buildings and Distribution Networks

In this paper, we present a co-simulation framework that combines two main simulation tools, one that provides detailed multiple building energy simulation ability with Energy-Plus being the core engine, and the other one that is a distribution level simulator, Matpower. Such a framework can be used to develop and study district level optimization techniques that exploit the interaction between a smart electric grid and buildings as well as the interaction between buildings themselves to achieve energy and cost savings and better energy management beyond what one can achieve through techniques applied at the building level only. We propose a heuristic algorithm to do load balancing in distribution networks affected by service restoration activities. Balancing is achieved through the use of utility directed usage of battery energy storage systems (BESS). This is achieved through demand response (DR) type signals that the utility communicates to individual buildings. We report simulation results on two test cases constructed with a 9-bus distribution network and a 57-bus distribution network, respectively. We apply the proposed balancing heuristic and show how energy storage systems can be used for temporary relief of impacted networks

An enhanced static-list scheduling algorithm for temporal partitioning onto RPUs

This paper presents a novel algorithm for temporal partitioning of graphs representing a behavioral description. The algorithm is based on an extension of the traditional static-list scheduling that tailors it to resolve both scheduling and temporal partitioning. The nodes to be mapped into a partition are selected based on a statically computed cost model. The cost for each node integrates communication effects, the critical path length, and the possibility of the critical path to hide the delay of parallel nodes. In order to alleviate the runtime there is no dynamic update of the costs. A comparison of the algorithm to other schedulers and with close-to-optimum results obtained with a simulated annealing approach is shown. The presented algorithm has been implemented and the results show that it is robust, effective, and efficient, and when compared to other methods finds very good results in small amounts of CPU time

Artificial intelligence (AI) methods in optical networks: A comprehensive survey

Producción CientíficaArtificial intelligence (AI) is an extensive scientific discipline which enables computer systems to solve problems by emulating complex biological processes such as learning, reasoning and self-correction. This paper presents a comprehensive review of the application of AI techniques for improving performance of optical communication systems and networks. The use of AI-based techniques is first studied in applications related to optical transmission, ranging from the characterization and operation of network components to performance monitoring, mitigation of nonlinearities, and quality of transmission estimation. Then, applications related to optical network control and management are also reviewed, including topics like optical network planning and operation in both transport and access networks. Finally, the paper also presents a summary of opportunities and challenges in optical networking where AI is expected to play a key role in the near future.Ministerio de Economía, Industria y Competitividad (Project EC2014-53071-C3-2-P, TEC2015-71932-REDT