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

Abstract

A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. Abstract: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 fitter 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.

SeedLight

“…Several RLC algorithm benchmarks have been implemented previously. These studies were performed on computers [20], smartphones [6] or high-end embedded devices, at least able to run a UNIX operating system [8]. There is not implementation on low powerful platform like ours.…”

A Modified Ant Colony Optimization Algorithm for Network Coding Resource Minimization

“…Hence, an explicit NCM subgraph can be built by a feasible individual. The BLS-based EAs include GAs [9], [10], [25], quantum-inspired EAs [26], [27], population based incremental learning [28], [29] and compact GA [30]. One of the disadvantages of BLS is that infeasible solutions account for the majority of the search space, which to a certain extent deteriorates the search ability and efficiency of EAs [31], [32].…”

An Improved MOEA/D for QoS Oriented Multimedia Multicasting with Network Coding

“…To explicitly see how data-flows pass through each of the intermediate nodes, the binary link state (BLS) chromosome representation is adopted. BLS-based representation has been widely used in NCM routing problems [9,10,11,12,15]. More details can be found in [15].…”

“…We evaluate the performance of the proposed algorithm on 8 random instances which are widely used in the literature [9,10,11,12,15]. These instances are all available at http://www.cs.nott.ac.uk/ rxq/benchmarks.htm and more details can be found in [11].…”

“…For example, the network coding resource minimization problem, which is for minimizing the involved computing resource, has drawn a lot of research attention [6,7,8,9,10,11,12]. Moreover, as coding and link costs both incur during the NCM, some researchers study the trade-off between them using multiobjective optimization approaches [13,14].…”