Evolutionary Approaches to Minimizing Network Coding Resources

We wish to minimize the resources used for network coding while achieving the desired throughput in a multicast scenario. We employ evolutionary approaches, based on a genetic algorithm, that avoid the computational complexity that makes the problem NP-hard. Our experiments show great improvements over the sub-optimal solutions of prior methods. Our new algorithms improve over our previously proposed algorithm in three ways. First, whereas the previous algorithm can be applied only to acyclic networks, our new method works also with networks with cycles. Second, we enrich the set of components used in the genetic algorithm, which improves the performance. Third, we develop a novel distributed framework. Combining distributed random network coding with our distributed optimization yields a network coding protocol where the resources used for coding are optimized in the setup phase by running our evolutionary algorithm at each node of the network. We demonstrate the effectiveness of our approach by carrying out simulations on a number of different sets of network topologies.Comment: 9 pages, 6 figures, accepted to the 26th Annual IEEE Conference on Computer Communications (INFOCOM 2007

An effective genetic algorithm for network coding

The network coding problem (NCP), which aims to minimize network coding resources such as nodes and links, is a relatively new application of genetic algorithms (GAs) and hence little work has so far been reported in this area. Most of the existing literature on NCP has concentrated primarily on the static network coding problem (SNCP). There is a common assumption in work to date that a target rate is always achievable at every sink as long as coding is allowed at all nodes. In most real-world networks, such as wireless networks, any link could be disconnected at any time. This implies that every time a change occurs in the network topology, a new target rate must be determined. The SNCP software implementation then has to be re-run to try to optimize the coding based on the new target rate. In contrast, the GA proposed in this paper is designed with the dynamic network coding problem (DNCP) as the major concern. To this end, a more general formulation of the NCP is described. The new NCP model considers not only the minimization of network coding resources but also the maximization of the rate actually achieved at sinks. This is particularly important to the DNCP, where the target rate may become unachievable due to network topology changes. Based on the new NCP model, an effective GA is designed by integrating selected new problem-specific heuristic rules into the evolutionary process in order to better diversify chromosomes. In dynamic environments, the new GA does not need to recalculate target rate and also exhibits some degree of robustness against network topology changes. Comparative experiments on both SNCP and DNCP illustrate the effectiveness of our new model and algorithm

Genetic Representations for Evolutionary Minimization of Network Coding Resources

We demonstrate how a genetic algorithm solves the problem of minimizing the resources used for network coding, subject to a throughput constraint, in a multicast scenario. A genetic algorithm avoids the computational complexity that makes the problem NP-hard and, for our experiments, greatly improves on sub-optimal solutions of established methods. We compare two different genotype encodings, which tradeoff search space size with fitness landscape, as well as the associated genetic operators. Our finding favors a smaller encoding despite its fewer intermediate solutions and demonstrates the impact of the modularity enforced by genetic operators on the performance of the algorithm.Comment: 10 pages, 3 figures, accepted to the 4th European Workshop on the Application of Nature-Inspired Techniques to Telecommunication Networks and Other Connected Systems (EvoCOMNET 2007

Cloud computing resource scheduling and a survey of its evolutionary approaches

A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon

An Evolutionary Computational Approach for the Problem of Unit Commitment and Economic Dispatch in Microgrids under Several Operation Modes

In the last decades, new types of generation technologies have emerged and have been gradually integrated into the existing power systems, moving their classical architectures to distributed systems. Despite the positive features associated to this paradigm, new problems arise such as coordination and uncertainty. In this framework, microgrids constitute an effective solution to deal with the coordination and operation of these distributed energy resources. This paper proposes a Genetic Algorithm (GA) to address the combined problem of Unit Commitment (UC) and Economic Dispatch (ED). With this end, a model of a microgrid is introduced together with all the control variables and physical constraints. To optimally operate the microgrid, three operation modes are introduced. The first two attend to optimize economical and environmental factors, while the last operation mode considers the errors induced by the uncertainties in the demand forecasting. Therefore, it achieves a robust design that guarantees the power supply for different confidence levels. Finally, the algorithm was applied to an example scenario to illustrate its performance. The achieved simulation results demonstrate the validity of the proposed approach.Ministerio de Ciencia, Innovación y Universidades TEC2016-80242-PMinisterio de Economía y Competitividad PCIN-2015-043Universidad de Sevilla Programa propio de I+D+

On minimizing coding operations in network coding based multicast: an evolutionary algorithm

In telecommunications networks, to enable a valid data transmission based on network coding, any intermediate node within a given network is allowed, if necessary, to perform coding operations. The more coding operations needed, the more coding resources consumed and thus the more computational overhead and transmission delay incurred. This paper investigates an efficient evolutionary algorithm to minimize the amount of coding operations required in network coding based multicast. Based on genetic algorithms, we adapt two extensions in the proposed evolutionary algorithm, namely a new crossover operator and a neighbourhood search operator, to effectively solve the highly complex problem being concerned. The new crossover is based on logic OR operations to each pair of selected parent individuals, and the resulting offspring are more likely to become feasible. The aim of this operator is to intensify the search in regions with plenty of feasible individuals. The neighbourhood search consists of two moves which are based on greedy link removal and path reconstruction, respectively. Due to the specific problem feature, it is possible that each feasible individual corresponds to a number of, rather than a single, valid network coding based routing subgraphs. The neighbourhood search is applied to each feasible individual to find a better routing subgraph that consumes less coding resource. This operator not only improves solution quality but also accelerates the convergence. Experiments have been carried out on a number of fixed and randomly generated benchmark networks. The results demonstrate that with the two extensions, our evolutionary algorithm is effective and outperforms a number of state-of-the-art algorithms in terms of the ability of finding optimal solutions

A quantum inspired evolutionary algorithm for dynamic multicast routing with network coding

This paper studies and models the multicast routing problem with network coding in dynamic network environment, where computational and bandwidth resources are to be jointly optimized. A quantum inspired evolutionary algorithm (QEA) is developed to address the problem above, where a restart scheme is devised for well adapting QEA for tracing the ever-changing optima in dynamic environment. Experimental results show that the proposed QEA outperforms a number of existing evolutionary algorithms in terms of the best solution obtained