Multicast Routing Problem Research Articles

Genetic algorithms for the QoS based multicast routing and wavelength allocation problem in WDM network

To tackle the QoS based multicast routing and wavelength allocation problem (MRWA), three multi-objective genetic algorithms are proposed, which are based on the ideas of Non-dominated Sorting, Strength Pareto and Decomposition, respectively. The chromosome coding scheme, crossover and mutation operators are redefined. To ensure the generated offspring being a connected light-tree, a light-path repair process and a loop eliminating process are designed. The proposed algorithms were evaluated on a set of different scale test problems and compared with the recently proposed GA based multi-objective optimization algorithm for this problem. The experimental results reveal very encouraging results in terms of the solution quality.

Genetic algorithm with ensemble of immigrant strategies for multicast routing in Ad hoc networks

In this paper, an Ensemble of Immigrant Strategies with Genetic Algorithm (EISGA) which optimizes the combined objectives of network lifetime and delay is proposed for solving multicast routing problem. Immigrant strategies are the specific replacement operators designed for dynamic optimization problems and are naturally suited for multicast routing in ad hoc networks. The proposed system ensembles random immigrant with random replacement, random immigrant with worst replacement, elitism-based immigrant and hybrid immigrant strategies. The sequence and topological coding with genetic operators such as modified topology crossover, energy mutation and node mutation are employed in EISGA. The performance of four variants of genetic algorithms formed from these immigrant strategies is evaluated in two different network topologies, with different range of immigrant probability values. Results show that fixing of probability values for various immigrant strategies is very difficult. The proposed EISGA, with equal probability and adaptive probability, is evaluated on four different networks with 10, 20, 30 and 40 nodes on two kinds of topologies. The performance of the proposed EISGA with adaptive probability is assessed in various Learning Period (LP) to determine the suitable LP and is compared with other existing algorithms using non-parametric statistical tests with average ranking. These results endorse that the proposed EISGA improves the performance of GA in solving multicast routing problems effectively.

Rendezvous point based approach to the multi-constrained multicast routing problem

The Quality of Service (QoS) routing requires a special approach to graph algorithms modeling. One of the mathematical concepts that reflects this class of problems is the multi-constrained minimum Steiner tree problem (MCMST). In this article, the RDP (named after the concept of the RenDezvouz Point), a novel algorithm for solving the MCMST problem is presented.

Multicast Routing Algorithm with QoS Constraints in Cognitive Wireless Mesh Networks

对认知无线Mesh 网络中满足QoS 约束的联合组播路由及频谱分配问题进行研究,提出了一个针对该问题的求解框架,包括问题描述、解决方案的表示、适应度函数以及频谱分配算法.基于两种具有代表性的智能计算方法:遗传算法、模拟退火,提出了两种满足端到端延迟约束的组播路由及频谱分配算法GA-MRSA 和SA-MRSA.这两种算法追求的目标是最小化组播树信道冲突总数,并且在获得较低的信道冲突数的情况下,还能占用较少的信道.仿真结果表明,所提出的两种算法能够达到预期目标,获得较低的信道冲突总数.;The problem of joining multicast routing and spectrum allocation with QoS constraints is studied in cognitive wireless mesh networks. A framework of solving the above problem, which contains a problem description, a representation of solution, fitness function, spectrum allocation algorithm, is proposed in this paper. Two algorithms for joint multicast routing and a spectrum allocation with end-to-end delay constraints based on intelligent computation are proposed in this paper. The first one is multicast routing and spectrum allocation algorithm based on genetic algorithm (GA-MRSA). The second one is multicast routing and spectrum allocation algorithm based on simulated annealing algorithm (SA-MRSA). The object of the two algorithms is to minimize the total channel conflict. Under the condition of getting lower total channel conflict number, the number of used channels is also few. Simulation results show that the two algorithms can achieve the expected goal: it can achieve a lower total channel conflict number.

A Multi-Objective Optimization Approach for Joint Channel Assignment and Multicast Routing in Multi-Radio Multi-Channel Wireless Mesh Networks

Multicast routing is an effective mechanism for delivering data to a group of receivers. Due to intrinsic property of air medium in wireless mesh networks (WMN), interference is an important issue in determining the data rate for multicast services. Interference reduction is handled by assigning multiple orthogonal channels to multiple radios in multi-radio multi-channel WMNs. Channel assignment is known to be a NP-complete problem. Most prior methods have solved multicast routing and channel assignment problems sequentially and have not considered the interplay between these two problems. Focusing on this issue, we address joint channel assignment and routing problem for multicast applications. In this paper, a novel technique based on a multi-objective genetic algorithm is proposed to build a delay constrained minimum cost multicast tree with minimum interference. We have examined the proposed algorithm on different network configurations. Experimental results demonstrate that our method finds better trees in terms of cost, delay, and interference compared to prior methods.

Sector Based Multicast Routing Algorithm for Mobile AD-HOC Networks

Multicast routing algorithms for mobile ad-hoc networks have been extensively researched in the recent past. In this paper, we present two algorithms for dealing with multicast routing problem using the notion of virtual forces. We look at the effective force exerted on a packet and determine whether a node could be considered as a Steiner node. The nodes' location information is used to generate virtual circuits corresponding to the multicast route. QoS parameters are taken into consideration in the form of virtual dampening force. The first algorithm produces relatively minimal multicast trees under the set of constraints. We improve upon the first algorithm and present a second algorithm that provides improvement in average residual energy in the network as well as effective cost per data packet transmitted. In this paper, the virtual-force technique has been applied for multicast routing for the first time in mobile ad-hoc networks.

Delay-constrained survivable multicast routing problem in WDM networks with shared segment-based protection

For a delay-constrained multicast transmission request, the goal of delay-constrained survivable multicast routing problem is to provide the primary multicast tree and the tree protecting sparse resources. The shared segment-based protection (SSBP) method is used in this article to protect the delay-constrained multicast transmission. Two heuristic methods are proposed to find the delay-constrained primary tree and the backup segments with delay constraint. Experiments are conducted to evaluate the performance of the proposed methods, and the performance to be evaluated includes wavelength efficiency ratio (WER), blocking ratio (BR), and executing time. Simulations show that the SSBP method can get better BR and WER than the previous results demonstrated Din and Jiang (Comput Commun 35(10):1172---1184, 2012).

A New Hybrid GRASP with the Pilot Method Forthe Delay-Constrained Multicast Routing Problem

Multicast routing problem is a well know optimization problem for transmitting real-time multimedia applications in telecommunication networks. As the underpinning mathematical model, the constrained minimum Steiner tree problem in graphs is a well-known NP-complete problem. In this paper we investigate a new hybrid GRASP (Greedy Randomized Adaptive Search Procedure) approach where a pilot method is applied to further enhance the search for the Delay-Constrained Least-Cost (DCLC) multicast routing problem. Experimental results demonstrate the efficiency of the hybrid GRASP algorithm and the contributions of the post-processing pilot method to better solutions in most cases. The proposed GRASP approach is a competitive approach in solving the DCLC multicast routing problem.

A Multicast Routing Algorithm with Cost, Delay and Bandwidth Constraints

Quality of service (QoS) generally assumes more than one QoS measure which implies that routing can be categorized as an instance of routing subject to multiple constraints: such as cost, delay, bandwidth, etc. The problem of constructing multicast trees is studied to meet the QoS requirements where it is necessary to provide bounded constraints among the source and all destinations while keeping the cost of the multicast tree low. So, a kind of source-destination QoS multicast routing problem is addressed about communication networks. The algorithm we presented takes bandwidth, delay and loss rate as premise, constructs routing selected function based on shortest path, modifies selected path according to the function above so as to fit multi-QoS parameters. Simulation results show that the algorithm has both lower delay and better performance and can be extended to cases of multiple QoS parameters conveniently.

Four-objective formulations of multicast flows via evolutionary algorithms with quality demands

In this work, we investigate two four-objective formulations of multicast routing problem, in which a tree must be set to deliver data to a subset of destination nodes in a network, optimizing several conflicting objectives. We propose a routing model based on SPEA2 (Strength Pareto Evolutionary Algorithm 2) to handle it, incorporating a heuristic that performs a reconnection step in crossover and mutation operators in order to produce a new tree. Three different heuristics were designed for such step. Experimental results were conducted to assess convergence and diversity goals over well-known instances of the problem, showing that the heuristic which alternates between shortest path and randomness produced the best results on most cases. It was shown that the proposed model compares well with traditional algorithms, namely, Dijkstra's algorithm and Takahashi-Matsuyama heuristic.

A Multiple Constrained QoS Routing Algorithm for the Wireless Sensor Networks

Routing problem is one of the most important issues to a wireless sensor network (WSN). It is the key problem to find an efficient energy strategy for prolonging network's lifetime because power supply might be impossible. This paper discusses the multicast routing problem of WSN with multiple QoS constraints, which may deal with the delay, bandwidth, hop count and packet reception rat and surplus energy metrics, and finds a minimum resource consumption path while satisfying multiple constraints optimization conditions, and describes a network model for researching the multicast routing problem. It presents a dynamic multicast routing algorithm with multiple QoS constraints (MCQoSRA). The MCQoSRA successfully solves the QoS routing problems when multicast nodes change dynamically in the networks. The MCQoSRA only requires the local state information of the link (or node), but does not require any global network sate to be maintained. In MCQoSRA, a multicast group member can join or leave the multicast session dynamically. The MCQoSRA can effectively decrease the overhead for constructing a multicast tree and the delay of the nodes, and improve the success ratio of seeking links. Simulation results show that the MCQoSRA provides an available means to implement multicast routing, and adapt to all kinds of the topology networks, and have better expansibility.

基于Markov链的最优多播机会路由算法

Improving the network capacity is one of the most important topics in the area of wireless networks. The broadcasting and fading feature of the wireless communications has not been sufficiently exploited in traditional routings. However, in opportunistic routing, they are able to be used to provide diversity. In this paper, the multicast opportunistic routing problem is studied in a Markov chain method, and the optimal algorithm called Least ETX Multicast Opportunistic Routing (LEMOR) is designed and proved to have the least end-to-end ETX. Simulation results show that LEMOR is able to significantly improve the network throughput by exploiting both opportunistic routing and multicast compared to the algorithms using either of them.

A Kind of New Multicast Routing Algorithm for Application of Internet of Things

Wireless Sensor Networks (WSN) is widely used as an effective medium to integrate physical world and information world of Internet of Things (IOT). While keeping energy consumption at a minimal level, WSN requires reliable communication. Multicasting is a general operation performed by the Base Station, where data is to be transmitted to a set of destination nodes. Generally, the packets are routed in a multi-hop approach, where some intermediate nodes are also used for packet forwarding. This problem can be reduced to the well-known Steiner tree problem, which has proven to be NP-complete for deterministic link descriptors and cost functions. In this paper, we propose a novel multicast protocol, named heuristic algorithms for the solution of the Quality of Service (QoS) constrained multicast routing problem, with incomplete information in Wireless Sensor Networks (WSN). As information aggregation or randomly fluctuating traffic loads, link measures are considered to be random variables. Simulation results show that the Hop Neural Networks (HNN) based heuristics with a properly chosen additive measures can yield to a good solution for this traditionally NP complex problem, when compared to the best multicast algorithms known.

Delay and Delay-Variation Constrained Multicast Routing Algorithm Based on Core Selection

With the development of multimedia network, more and more real-time multimedia applications require a network capable of satisfying QoS constraints such as delay and delay-variation bound so that the messages reach each destination node at almost the same time within a certain specified time limit, and certain application also demand that different streams for each different destination node should reach with minimum cost under the constraints. Therefore, the delay and delay-variation constraint multicast routing problem is studied and a new strategy in core selection methods is proposed. The algorithm we propose guarantees that the delay from the source to any destination does not exceed a real-time constraint satisfying the delay-variation constraint under cost minimization. In addition, this core selection methods achieve a balance of optimizing cost and delay of the multicast tree. Simulation results show that the algorithm has low complexity and balances between the computational complexity and performance, it can also meet the requirements of real-time network communication.

On multi-stream multi-source multicast routing

Multicasting has been used to conserve bandwidth and reduce network traffic for delivering a single data stream to a set of destinations. The problem of conserving bandwidth is a challenging one when one considers multiple data streams with multiple sources for each and a set of destinations that subscribe to one or more of these streams. The Multi-stream Multi-source Multicast Routing Problem (MMMRP) is to determine multiple multicasting trees on a given network, rooted at sources (nodes in the network) that are responsible for delivering one or more data streams to a set of destinations. Since several multicast trees co-exist on the same network, our goal is to construct these trees in such a way that the minimum 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 MMMRP with known algorithms for the multicast tree packing problem and our exhaustive empirical evaluations show that our heuristic has a very low execution-time while achieving near-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 that are based on Steiner tree heuristics.

QoS-driven multicast routing in sparse-splitting optical networks

This paper investigates the quality-of-service (QoS)-driven multicast routing problem in a sparse-splitting optical network. The main objective is to minimize the total cost of wavelength channels utilized by the light-tree while satisfying required QoS parameters. In this paper, both the optical-layer constraints (e.g., optical signal power) and application-layer requirements (e.g., end-to-end delay and inter-destination delay variation) are considered as the QoS parameters. First, integer linear programming (ILP) formulations to solve the optimal multicast routing problem with the given QoS parameters are presented. Solving the ILP formulations for large-scale networks can easily overwhelm the capabilities of state-of-the-art computing facilities, and hence, a heuristic algorithm is proposed to construct a feasible light-tree that satisfies the required QoS parameters in large-scale networks. Simulation results demonstrate the performance of the proposed heuristic algorithm in terms of the cost of utilized wavelength channels.

A simulated annealing based genetic local search algorithm for multi-objective multicast routing problems

This paper presents a new hybrid evolutionary algorithm to solve multi-objective multicast routing problems in telecommunication networks. The algorithm combines simulated annealing based strategies and a genetic local search, aiming at a more flexible and effective exploration and exploitation in the search space of the complex problem to find more non-dominated solutions in the Pareto Front. Due to the complex structure of the multicast tree, crossover and mutation operators have been specifically devised concerning the features and constraints in the problem. A new adaptive mutation probability based on simulated annealing is proposed in the hybrid algorithm to adaptively adjust the mutation rate according to the fitness of the new solution against the average quality of the current population during the evolution procedure. Two simulated annealing based search direction tuning strategies are applied to improve the efficiency and effectiveness of the hybrid evolutionary algorithm. Simulations have been carried out on some benchmark multi-objective multicast routing instances and a large amount of random networks with five real world objectives including cost, delay, link utilisations, average delay and delay variation in telecommunication networks. Experimental results demonstrate that both the simulated annealing based strategies and the genetic local search within the proposed multi-objective algorithm, compared with other multi-objective evolutionary algorithms, can efficiently identify high quality non-dominated solution set for multi-objective multicast routing problems and outperform other conventional multi-objective evolutionary algorithms in the literature.

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.

Mathematical programming approaches for dual multicast routing problem with multilayer risk cost

This paper addresses a dual multicast routing problem with shared risk link group (SRLG) diverse costs (DMR-SRLGD) that arises from large-scale distribution of realtime multicast data (e.g., internet protocol TV, videocasting, online games, stock price update). The goal of this problem is to find two redundant multicast trees, each from one of the two sources to every destination at a minimum cost. The cost of the problem contains two parts: the multicast routing cost and the shard common risk cost. Such common risk could cause the failures of multiple links simultaneously. Therefore, the DMR-SRLGD ensures the availability and reliability of multicast service. We formulate an edge-based model for the DMR-SRLGD. In addition, we also propose a path-based model that rises from the Dantzig-Wolfe decomposition of the edge-based model, and develop a column-generation framework to solve the linear relaxation of the path-based formulation. We then employ a branch-and-price solution method to find integer solutions to DMR-SRLGD. We also extend both edge-based and path-based models to handle the complex quality of service requirements. The computational results show the edge-based model is superior than the path-based model for the easy and small test instances, whereas the path-based model provides better solutions in a timely fashion for hard or large test instances.

QoS Multicast Routing Algorithm Based on Crowding Ant Colony Algorithm

The basic ant colony algorithm is easy to fall into local optimum and its convergent speed is slow for solving multiple QoS multicast routing problems. Therefore, a crowding ant colony algorithm is proposed in this paper to solve the problems. Crowded degree in artificial fish swarm algorithm is used to adjust nodes transition strategy dynamically according to the congestion of nodes. Stagnation behavior is judged by the similarity of multicast tree and chaos perturbation is utilized to update the pheromone trail on the multicast tree that may fall into local optimum in order that solutions can range out of local optimum. According to simulations, the global search is enhanced at the initial and convergence rate has improved greatly at the later. The improved algorithm is feasible and effective.