ON MULTIMEDIA CONTENT DELIVERY AND MULTICASTING

Multimedia content now contribute to a huge amount of the Internet traffic due to the popularity and availability of anytime anywhere Internet connection. Unlike the circuit-switched telephone network - in which necessary resources are reserved for communication between two parties at the time the connection is established, a packet-switched network, like the Internet, only guarantees the reachability when the connection between two parties is established. In other words, the end-to-end delay and available bandwidth between two hosts depend on the amount of traffic on the network. The communication paths between the participating hosts are also determined by the routing policies and hence are not under control of the participating hosts. Hence how to improve the performance of delivering multimedia content on the Internet has become an interesting research topic.In this dissertation, we consider the problem of delivering multimedia contents using multicast wherein a group of participants are participating in the same com- munication session. We assume the networks are flexible such that the end hosts can specify the communication paths. A few examples of this type of networks are overlay networks and IPv6 network with source routing support. This problem is addressed from both routing and network traffic perspectives.First, we assume a two-layer approach which includes a well-provisioned service overlay network and the regular Internet. The participants in the multimedia group communication can take the advantage of the service overlay network by connecting to the nodes in the service overlay network through the Internet. We consider two major assignment problems - Server and Client Assignment Problem (SCAP, Client- Server model) and Client Assignment Problem (CAP, Peer-to-Peer model) as well as several variants of these problems. These problems are NP-hard and we have developed polynomial-time heuristic algorithms to assign the participants to appropriate service nodes such that some real-time constraint(s) are satisfied and the number of service nodes involved are minimal. Integer programming (IP) models for solving these problems are also developed for performance evaluation purpose. Empirical results show that the solution quality of the proposed algorithms compares favorably with the optimal ones obtained from the execution of IP models, while keeping the execution times significantly low.We have also considered the Multi-stream Multi-source Multicast Routing Prob- lem ( MMMRP). Given a network and a set of multicast sessions, each with one or more sources and multiple destinations. The goal of MMMRP is to determine mul- tiple multicast tree for these multicast sessions on the given network in such a way that the overall residual bandwidth on the links that are shared among the trees is maximized. We prove that MMMRP is NP-hard and apart from providing an IP formulation, we have also provided a heuristic algorithm MMForests which runs in polynomial-time. We compared and contrasted the performance of MMMRP with known algorithms for the multicast tree packing problem. Our exhaustive empirical evaluations show that our heuristic has a very low execution-time while achieving the optimal residual bandwidth. In addition, our heuristic is very scalable as it is able to produce results for networks with thousands of nodes, unlike the other ones which are based on Steiner tree heuristics

Roteamento multicast multisessão: modelos e algoritmos

Multicast Technology has been studied over the last two decades and It has shown to be a good approach to save network resources. Many approaches have been considered to solve the multicast routing problem considering only one session and one source to attending session‘s demand, as well, multiple sessions with more than one source per session. In this thesis, the multicast routing problem is explored taking in consideration the models and the algorithms designed to solve it when where multiple sessions and sources. Two new models are proposed with different focuses. First, a mono-objective model optimizing residual capacity, Z, of the network subject to a budget is designed and the objective is to maximize Z. Second, a multi-objective model is designed with three objective functions: cost, Z and hops counting. Both models consider multisession scenario with one source per session. Besides, a third model is examined. This model was designed to optimize Z in a scenario with multiple sessions with support to more than one source per session. An experimental analysis was realized over the models considered. For each model, a set of algorithms were designed. First, an ACO, a Genetic algorithm, a GRASP and an ILS algorithm were designed to solve the mono-objective model – optimizing Z subject to a budget. Second, a set of algorithm were designed to solve the multi-objective model. The classical approaches were used: NSGA2, ssNSGA2, SMS-EMOA, GDE3 and MOEA/D. In addition, a transgenetic algorithm was designed to solve the problem and it was compared against the classical approaches. This algorithm considers the use of subpopulations during the evolution. Each subpopulation is based on a solution construction operator guided by one of the objective functions. Some solutions are considered as elite solutions and they are considered to be improved by a transposon operator. Eight versions of the transgenetic algorithm were evaluated. Third, an algorithm was designed to solve the problem with multiple sessions and multiple sources per sessions. This algorithm is based on Voronoi Diagrams and it is called MMVD. The algorithm designed were evaluated on large experimental analysis. The sample generated by each algorithm on the instances were evaluated based on non-parametric statistical tests. The analysis performed indicates that ILS and Genetic algorithm have outperformed the ACO and GRASP. The comparison between ILS and Genetic has shown that ILS has better processing time performance. In the multi-objective scenario, the version of Transgenetic called cross0 has shown to be statistically better than the other algorithms in most of the instances based on the hypervolume and addictive/multiplicative epsilon quality indicators. Finally, the MMVD algorithm has shown to be better than the algorithm from literature based on the experimental analysis performed for the model with multiple session and multiple sources per session.A tecnologia multicast tem sido amplamente estudada ao longo dos anos e apresenta-se como uma solução para melhor utilização dos recursos da rede. Várias abordagens já foram avaliadas para o problema de roteamento desde o uso de uma sessão com apenas uma fonte a um cenário com múltiplas sessões e múltiplas fontes por sessão. Neste trabalho, é feito um estudo dos modelos matemáticos para o problema com múltiplas sessões e múltiplas fontes. Dois modelos matemáticos foram propostos: uma versão multissessão mono-objetivo que visa a otimização da capacidade residual sujeito a um limite de custo e uma versão multiobjetivo com três funções-objetivo. Ambos os modelos levam em conta o cenário multissessão com uma fonte por sessão. Além disso, um estudo algorítmico foi realizado sobre um modelo da literatura que utiliza múltiplas fontes por sessão. Três conjuntos de algoritmos foram propostos. O primeiro conjunto trata do problema mono-objetivo proposto e considera as abordagens ACO, Genético, GRASP e ILS. O segundo conjunto consiste dos algoritmos propostos para o modelo multiobjetivo. Foram projetados os seguintes algoritmos: NSGA2, ssNSGA2, GDE3, MOEA/D e SMS-EMOA. Além disso, foi projetado um algoritmo transgenético com subpopulações baseadas em operadores de criação de solução direcionados por objetivos do problema. Também foi utilizado o conceito de soluções de elite. No total, 8 versões do algoritmo transgenético foram avaliadas. O terceiro conjunto de algoritmos consiste da heurística MMVD proposta para o modelo da literatura com múltiplas fontes por sessão. Esta heurística é baseada no uso de diagramas de Voronoi. O processo experimental foi realizado com amplo número de instâncias configuradas de modo a avaliar diferentes situações. Os resultados foram comparados utilizando métodos estatísticos não-paramétricos. A análise final indicou que o ILS e o Genético obtiveram resultados muito similares, entretanto o ILS possui melhor tempo de processamento. A versão cross0 do algoritmo transgenético obteve o melhor resultado em praticamente todos os cenários avaliados. A heurística MMVD obteve excelentes resultados sobre algoritmos da literatura