Distributed control of coded networks

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 97-101).The introduction of network coding has the potential to revolutionize the way people operate networks. For the benefits of network coding to be realized, distributed solutions are needed for various network problems. In this work, we look at three aspects of distributed control of coded networks. The first one is distributed algorithms for establishing minimum-cost multicast connections in coded networks. The subgraph optimization problem can be viewed as an linear optimization problem, and we look at algorithms that solve this problem for both static and dynamic multicasts. For static multicast, we present decentralized dual subgradient algorithms to find the min-cost subgraph. Due to the special structure of the network coding problem, we can recover a feasible primal solution after each iteration, and also derive theoretical bounds on the convergence rate in both the dual and the primal spaces. In addition, we propose heuristics to further improve our algorithm, and demonstrate through simulations that the distributed algorithm converges to the optimal subgraph quickly and is robust against network topology changes. For dynamic multicast, we introduce two types of rearrangements, link rearrangement and code rearrangement, to characterize disturbances to users. We present algorithms to solve the online network coding problem, and demonstrate through simulations that the algorithms can adapt to changing demands of the multicast group while minimizing disturbances to existing users.(cont.) The second part of our work focuses on analysis of COPE, a distributed opportunistic network coding system for wireless mesh networks. Experiments have shown that COPE can improve network throughput significantly, but current theoretical analysis fails to fully explain this performance. We argue that the key factor that shapes COPE's performance curve is the interaction between COPE and the MAC protocol. We also propose a simple modification to COPE that can further increase the network throughput. Finally, we study network coding for content distribution in peer-to-peer networks. Such systems can improve the speed of downloads and the robustness of the systems. However, they are very vulnerable to Byzantine attacks, and we need to have a signature scheme that allows nodes to check the validity of a packet without decoding. In this work, we propose such a signature scheme for network coding. Our scheme makes use of the linearity property of the packets in a coded system, and allows nodes to check the integrity of the packets received easily. We show that the proposed scheme is secure, and its overhead is negligible for large files.by Fang Zhao.Ph.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 nondominated sorting genetic algorithm for bi-objective network coding based multicast routing problems

Network coding is a new communication technique that generalizes routing, where, instead of simply forwarding the packets they receive, intermediate nodes are allowed to recombine (code) together some of the data packets received from different incoming links if necessary. By doing so, the maximum information flow in a network can always be achieved. However, performing coding operations (i.e. recombining data packets) incur computational overhead and delay of data processing at the corresponding nodes. In this paper, we investigate the optimization of the network coding based multicast routing problem with respect to two widely considered objectives, i.e. the cost and the delay. In general, reducing cost can result into a cheaper multicast solution for network service providers, while decreasing delay improves the service quality for users. Hence we model the problem as a bi-objective optimization problem to minimize the total cost and the maximum transmission delay of a multicast. This bi-objective optimization problem has not been considered in the literature. We adapt the Elitist Nondominated Sorting Genetic Algorithm (NSGA-II) for the new problem by introducing two adjustments. As there are many infeasible solutions in the search space, the first adjustment is an initialization scheme to generate a population of feasible and diversified solutions. These initial solutions help to guide the search towards the Pareto-optimal front. In addition, the original NSGA-II is very likely to produce a number of solutions with identical objective values at each generation, which may seriously deteriorate the level of diversity and the optimization performance. The second adjustment is an individual delegate scheme where, among those solutions with identical objective values, only one of them is retained in the population while the others are deleted. Experimental results reveal that each adopted adjustment contributes to the adaptation of NSGA-II for the problem concerned. Moreover, the adjusted NSGA-II outperforms a number of state-of-the-art multiobjective evolutionary algorithms with respect to the quality of the obtained nondominated solutions in the conducted experiments

An asymptotically optimal push-pull method for multicasting over a random network

We consider allcast and multicast flow problems where either all of the nodes or only a subset of the nodes may be in session. Traffic from each node in the session has to be sent to every other node in the session. If the session does not consist of all the nodes, the remaining nodes act as relays. The nodes are connected by undirected links whose capacities are independent and identically distributed random variables. We study the asymptotics of the capacity region (with network coding) in the limit of a large number of nodes, and show that the normalized sum rate converges to a constant almost surely. We then provide a decentralized push-pull algorithm that asymptotically achieves this normalized sum rate without network coding.Comment: 13 pages, extended version of paper presented at the IEEE International Symposium on Information Theory (ISIT) 2012, minor revision to text to address review comments, to appear in IEEE Transactions in information theor

Network coding via evolutionary algorithms

Network coding (NC) is a relatively recent novel technique that generalises network operation beyond traditional store-and-forward routing, allowing intermediate nodes to combine independent data streams linearly. The rapid integration of bandwidth-hungry applications such as video conferencing and HDTV means that NC is a decisive future network technology. NC is gaining popularity since it offers significant benefits, such as throughput gain, robustness, adaptability and resilience. However, it does this at a potential complexity cost in terms of both operational complexity and set-up complexity. This is particularly true of network code construction. Most NC problems related to these complexities are classified as non deterministic polynomial hard (NP-hard) and an evolutionary approach is essential to solve them in polynomial time. This research concentrates on the multicast scenario, particularly: (a) network code construction with optimum network and coding resources; (b) optimising network coding resources; (c) optimising network security with a cost criterion (to combat the unintentionally introduced Byzantine modification security issue). The proposed solution identifies minimal configurations for the source to deliver its multicast traffic whilst allowing intermediate nodes only to perform forwarding and coding. In the method, a preliminary process first provides unevaluated individuals to a search space that it creates using two generic algorithms (augmenting path and linear disjoint path. An initial population is then formed by randomly picking individuals in the search space. Finally, the Multi-objective Genetic algorithm (MOGA) and Vector evaluated Genetic algorithm (VEGA) approaches search the population to identify minimal configurations. Genetic operators (crossover, mutation) contribute to include optimum features (e.g. lower cost, lower coding resources) into feasible minimal configurations. A fitness assignment and individual evaluation process is performed to identify the feasible minimal configurations. Simulations performed on randomly generated acyclic networks are used to quantify the performance of MOGA and VEGA

An improved MOEA/D algorithm for multi-objective multicast routing with network coding

Network coding enables higher network throughput, more balanced traffic, and securer data transmission. However, complicated mathematical operations incur when packets are combined at intermediate nodes, which, if not operated properly, lead to very high network resource consumption and unacceptable delay. Therefore, it is of vital importance to minimize various network resources and end-to-end delays while exploiting promising benefits of network coding. Multicast has been used in increasingly more applications, such as video conferencing and remote education. In this paper the multicast routing problem with network coding is formulated as a multi-objective optimization problem (MOP), where the total coding cost, the total link cost and the end-to-end delay are minimized simultaneously. We adapt the multi-objective evolutionary algorithm based on decomposition (MOEA/D) for this MOP by hybridizing it with a population-based incremental learning technique which makes use of the global and historical information collected to provide additional guidance to the evolutionary search. Three new schemes are devised to facilitate the performance improvement, including a probability-based initialization scheme, a problem-specific population updating rule, and a hybridized reproduction operator. Experimental results clearly demonstrate that the proposed algorithm outperforms a number of state-of-the-art MOEAs regarding the solution quality and computational time

Selection of network coding nodes for minimal playback delay in streaming overlays

Network coding permits to deploy distributed packet delivery algorithms that locally adapt to the network availability in media streaming applications. However, it may also increase delay and computational complexity if it is not implemented efficiently. We address here the effective placement of nodes that implement randomized network coding in overlay networks, so that the goodput is kept high while the delay for decoding stays small in streaming applications. We first estimate the decoding delay at each client, which depends on the innovative rate in the network. This estimation permits to identify the nodes that have to perform coding for a reduced decoding delay. We then propose two iterative algorithms for selecting the nodes that should perform network coding. The first algorithm relies on the knowledge of the full network statistics. The second algorithm uses only local network statistics at each node. Simulation results show that large performance gains can be achieved with the selection of only a few network coding nodes. Moreover, the second algorithm performs very closely to the central estimation strategy, which demonstrates that the network coding nodes can be selected efficiently in a distributed manner. Our scheme shows large gains in terms of achieved throughput, delay and video quality in realistic overlay networks when compared to methods that employ traditional streaming strategies as well as random network nodes selection algorithms.Comment: submitted to IEEE Transactions on Multimedia, January 18th 201

Sur l'utilisation du codage réseau et du multicast pour améliorer la performance dans les réseaux filaires

La popularité de la grande variété de l'utilisation d'Internet entraîne une croissance significative du trafic de données dans les réseaux de télécommunications. L'efficacité de la transmission de données sera contestée en vertu du principe de la capacité actuelle du réseau et des mécanismes de contrôle de flux de données. En plus d'augmenter l'investissement financier pour étendre la capacité du réseau, améliorer les techniques existantes est plus rationnel et éconmique.Diverses recherches de pointe pour faire face aux besoins en évolution des réseaux ont vu le jour, et l'une d'elles est appelée codage de réseau. Comme une extension naturelle dans la théorie du codage, il permet le mélange de différents flux réseau sur les noeuds intermédiaires, ce qui modifie la façon d'éviter les collisions de flux de données. Il a été appliqué pour obtenir un meilleur débit, fiabilité, sécurité et robustesse dans différents environnements et applications réseau. Cette thèse porte sur l'utilisation du réseau de codage pour le multicast dans les réseaux maillés fixes et systèmes de stockage distribués. Nous avons d'abord des modèles de différentes stratégies de routage multicast dans un cadre d'optimisation, y compris de multicast à base d'arbres et de codage de réseau; nous résolvons les modèles avec des algorithmes efficaces et comparons l'avantage de codage, en termes de gain de débit de taille moyenne graphique généré aléatoirement. Basé sur l'analyse numérique obtenue à partir des expériences précédentes, nous proposons un cadre révisé de routage multicast, appelé codage de réseau stratégique, qui combine transmission muticast standard et fonctions de codage de réseau afin d'obtenir le maximum de bénéfice de codage réseau au moindre coût lorsque ces coûts dépendent à la fois sur le nombre de noeuds à exécuter un codage et le volume de trafic qui est codé. Enfin, nous étudions le problème révisé de transport qui est capable de calculer un système de routage statique entre les serveurs et les clients dans les systèmes de stockage distribués où nous appliquons le codage pour soutenir le stockage de contenu. Nous étendons l'application à un problème d'optimisation général, nommé problème de transport avec des contraintes de degré, qui peut être largement utilisé dans divers domaines industriels, y compris les télécommunications, mais n'a pas été étudié très souvent. Pour ce problème, nous obtenons quelques résultats théoriques préliminaires et nous proposons une approche de décomposition Lagrange raisonnableThe popularity of the great variety of Internet usage brings about a significant growth of the data traffic in telecommunication network. Data transmission efficiency will be challenged under the premise of current network capacity and data flow control mechanisms. In addition to increasing financial investment to expand the network capacity, improving the existing techniques are more rational and economical. Various cutting-edge researches to cope with future network requirement have emerged, and one of them is called network coding. As a natural extension in coding theory, it allows mixing different network flows on the intermediate nodes, which changes the way of avoiding collisions of data flows. It has been applied to achieve better throughput and reliability, security, and robustness in various network environments and applications. This dissertation focuses on the use of network coding for multicast in fixed mesh networks and distributed storage systems. We first model various multicast routing strategies within an optimization framework, including tree-based multicast and network coding; we solve the models with efficient algorithms, and compare the coding advantage, in terms of throughput gain in medium size randomly generated graphs. Based on the numerical analysis obtained from previous experiments, we propose a revised multicast routing framework, called strategic network coding, which combines standard multicast forwarding and network coding features in order to obtain the most benefit from network coding at lowest cost where such costs depend both on the number of nodes performing coding and the volume of traffic that is coded. Finally, we investigate a revised transportation problem which is capable of calculating a static routing scheme between servers and clients in distributed storage systems where we apply coding to support the storage of contents. We extend the application to a general optimization problem, named transportation problem with degree constraints, which can be widely used in different industrial fields, including telecommunication, but has not been studied very often. For this problem, we derive some preliminary theoretical results and propose a reasonable Lagrangian decomposition approachEVRY-INT (912282302) / SudocSudocFranceF