Optimal multicast route packing

This paper considers the hop-constrained multicast route packing problem with a bandwidth reservation to build QoS-guaranteed multicast routing trees with a minimum installation cost. Given a set of multicast sessions, each of which has a hop limit constraint and a bandwidth requirement, the problem is to determine the set of multicast routing trees in an arc-capacitated network with the objective of minimizing the cost. For the problem, we propose a branch-and-cut-and-price algorithm, which can be viewed as a branch-and-bound method incorporating both the strong cutting plane algorithm and the column generation method. We implemented and tested the proposed algorithm on randomly generated problem instances with sizes up to 30 nodes, 570 arcs, and 10 multicast sessions. The test results show that the algorithm can obtain the optimal solution to practically sized problem instances within a reasonable time limit in most cases.     

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.     

Routing-efficient CDS construction in Disk-Containment Graphs

In wireless networks, Connected Dominating Sets (CDSs) are widely used as virtual backbones for communications. On one hand, reducing the backbone size will reduce the maintenance overhead. So how to minimize the CDS size is a critical issue. On the other hand, when evaluating the performance of a wireless network, the hop distance between two communication nodes, which reflect the energy consumption and response delay, is of great importance. Hence how to minimize the routing cost is also a key problem for constructing the network virtual backbone. In this paper, we study the problem of constructing applicable CDS in wireless networks in terms of size and routing cost. We formulate a wireless network as a Disk-Containment Graph (DCG), which is a generalization of the Unit-Disk Graph (UDG), and we develop an efficient algorithm to construct CDS in such kind of graphs. The algorithm contains two parts and is flexible to balance the performance between the two metrics. We also analyze the algorithm theoretically. It is shown that our algorithm has provable performance in minimizing the CDS size and reducing the communication distance for routing.     

A multicast routing algorithm based on searching in directed graph

Aiming at constructing a delay and delay variation bounded Steiner tree in the real-time streaming media communication, in this paper, we discuss a multicast routing algorithm based on searching a directed graph (MRASDH). During the process of the construction of the multicast tree, some nodes and links in the network topology do not affect the outcome of the constructed tree. Therefore, based on the thought of shrinking the search space through deleting these non-relative nodes and edges to the utmost, the ant algorithm is utilized to generate a directed sub-graph of the network topology for each destination node, in which each node owns a bounded out-degree. And all these sub-graphs can be merged into a new directed graph that serves as the new search space. In the new space, the simulated annealing algorithm is applied to obtain a multicast tree that satisfies the condition for the optimization. The performance analysis and simulation results demonstrate that this algorithm can effectively construct a delay and delay variation bounded multicast tree. They also show that the algorithm have lower time complexity than the current ones, which means a much better result would be achieved when the system scale rises greatly.     

Optimization architecture for joint multi-path routing and scheduling in wireless mesh networks

In Wireless Mesh Networks (WMN), the optimal routing of data depends on the link capacities which are determined by link scheduling. The optimal performance of the network, therefore, can only be achieved by joint routing and scheduling optimization. Although the joint single-path routing and scheduling optimization problem has been extensively studied, its multi-path counterpart within wireless mesh networks has not yet been fully investigated. In this paper, we present an optimization architecture for joint multi-path QoS routing and the underlying wireless link scheduling in wireless mesh networks. By employing the contention matrix to represent the wireless link interference, we formulate a utility maximization problem for the joint multi-path routing and MAC scheduling and resolve it using the primal–dual method. Since the multi-path routing usually results in the non-strict concavity of the primal objective function, we first introduce the Proximal Optimization Algorithm to get around such difficulty. We then propose an algorithm to solve the routing subproblem and the scheduling subproblem via the dual decomposition. Simulations demonstrate the efficiency and correctness of our algorithm.     

Dynamic reconfiguration in multigroup multicast routing under uncertainty

Motivated by high quality multimedia and remote collaborative environments, we solve the problem of multigroup multicast routing with a number of features important for real-world deployment of the interactive media technologies. Based on the ant colony optimization metaheuristic, our algorithm is the first to work with uncertain knowledge of underlying network capabilities and support on-the-fly media transcoding inside the multicast tree. New contributions of this work are described next. We present two extensions of the algorithm, which improve quality of the solution. We introduce an integrated approach to solution of the problem, which is effective for both original solving from scratch as well as for new dynamic reconfiguration of multicast tree, where minimum perturbation of existing solution is desired. Experiments show that our algorithm is not only successful in maintaining existing communication with low number of unnecessary disruptions, but also capable of keeping the multicast trees efficient.     

On 1-rate and 2-rate multicast 3-stage Clos networks

The 3-stage Clos network C（n, m, r） is considered as the most basic and popular multistage interconnection network which has been widely employed for data communications and parallel computing systems. Quite a lot of efforts has been put on the research of the 3- stage Clos network. Unfortunately, very little is known for the multirate multicast Clos network which is the most complicated case. Firstly a sufficient condition for 1-rate multicast networks to be SNB is given, from which a result for 2-rate multicast networks to be WSNB can easily be gotten. Furthermore, by using a reservation-scheme routing, more specific result for 2-rate multicast networks to be WSNB can be obtained for the case of one of them exceeding 1/2.     

Network delay inference from additive metrics

We use computational phylogenetic techniques to solve a central problem in inferential network monitoring. More precisely, we design a novel algorithm for multicast‐based delay inference, that is, the problem of reconstructing delay characteristics of a network from end‐to‐end delay measurements on network paths. Our inference algorithm is based on additive metric techniques used in phylogenetics. It runs in polynomial time and requires a sample of size only poly(log n). We also show how to recover the topology of the routing tree. © 2010 Wiley Periodicals, Inc. Random Struct. Alg., 2010     

Particle swarm optimization for the Steiner tree in graph and delay-constrained multicast routing problems

This paper presents the first investigation on applying a particle swarm optimization (PSO) algorithm to both the Steiner tree problem and the delay constrained multicast routing problem. Steiner tree problems, being the underlining models of many applications, have received significant research attention within the meta-heuristics community. The literature on the application of meta-heuristics to multicast routing problems is less extensive but includes several promising approaches. Many interesting research issues still remain to be investigated, for example, the inclusion of different constraints, such as delay bounds, when finding multicast trees with minimum cost. In this paper, we develop a novel PSO algorithm based on the jumping PSO (JPSO) algorithm recently developed by Moreno-Perez et al. (Proc. of the 7th Metaheuristics International Conference, 2007), and also propose two novel local search heuristics within our JPSO framework. A path replacement operator has been used in particle moves to improve the positions of the particle with regard to the structure of the tree. We test the performance of our JPSO algorithm, and the effect of the integrated local search heuristics by an extensive set of experiments on multicast routing benchmark problems and Steiner tree problems from the OR library. The experimental results show the superior performance of the proposed JPSO algorithm over a number of other state-of-the-art approaches.     

几种特殊结构全光网中多播路由的波长分配算法

研究全光WDM网络中多播请求的路由与波长分配问题.给定网络拓扑和一组多播通信请求,要求对其进行路由和波长分配,满足波长连续性和波长无冲突约束,使得所用的波长总数最少.就几类特殊网络进行了研究.首先对二分树网络进行了研究,此时问题是多项式时间可求解的.其次对树网络进行了讨论,证明了即使是星网络,问题也不存在近似比小于m1/2-ρ(0＜ρ＜1))的近似算法,除非NP=ZPP,这里m是星图的边数.随后给出了近似比为(√m 1)(log r/√m 1 1)的近似算法,此结果对一般图也成立.最后考虑了环网和树环网,给出了近似比为3.6和2△的近似算法,这里△是图的最大度.     

On routing in VLSI design and communication networks

In this paper, we study the global routing problem in VLSI design and the multicast routing problem in communication networks. First we propose new and realistic models for both problems. In the global routing problem in VLSI design, we are given a lattice graph and subsets of the vertex set. The goal is to generate trees spanning these vertices in the subsets to minimize a linear combination of overall wirelength (edge length) and the number of bends of trees with respect to edge capacity constraints. In the multicast routing problem in communication networks, a graph is given to represent the network, together with subsets of the vertex set. We are required to find trees to span the given subsets and the overall edge length is minimized with respect to capacity constraints. Both problems are APX-hard. We present the integer linear programming (LP) formulation of both problems and solve the LP relaxations by the fast approximation algorithms for min-max resource-sharing problems in [K. Jansen, H. Zhang, Approximation algorithms for general packing problems and their application to the multicast congestion problem, Math. Programming, to appear, doi:10.1007/s10107-007-0106-8] (which is a generalization of the approximation algorithm proposed by Grigoriadis and Khachiyan [Coordination complexity of parallel price-directive decomposition, Math. Oper. Res. 2 (1996) 321-340]). For the global routing problem, we investigate the particular property of lattice graphs and propose a combinatorial technique to overcome the hardness due to the bend-dependent vertex cost. Finally, we develop asymptotic approximation algorithms for both problems with ratios depending on the best known approximation ratio for the minimum Steiner tree problem. They are the first known theoretical approximation bound results for the problems of minimizing the total costs (including both the edge and the bend costs) while spanning all given subsets of vertices.     

对一类overlay层组播路由问题的模型的改进

根据overlay层虚拟网图的特点，本给出了一类overlay层组播路由问题的数学模型的改进，及相应的一种启发式算法，即MMD算法，并分析了该算法的性质，证明了它是一个多项式时间算法。     

A model for the capacitated,hop-constrained,per-packet wireless mesh network design problem

Three critical factors in wireless mesh network design are the number of hops between supply and demand points, the bandwidth capacity of the transport media, and the technique used to route packets within the network. Most previous research on network design has focused on the issue of hop constraints and/or bandwidth capacity in wired networks while assuming a per-flow routing scheme. However, networks that employ per-packet routing schemes in wireless networks involve different design issues that are unique to this type of problem. We present a methodology for designing wireless mesh networks that consider bandwidth capacity, hop constraints, and profitability for networks employing a per-packet routing system.     

Minimum power multicasting in wireless networks under probabilistic node failures

In this paper we deal with a probabilistic extension of the minimum power multicast (MPM) problem for wireless networks. The deterministic MPM problem consists in assigning transmission powers to the nodes, so that a multihop connection can be established between a source and a given set of destination nodes and the total power required is minimized. We present an extension to the basic problem, where node failure probabilities for the transmission are explicitly considered. This model reflects the necessity of taking uncertainty into account in the availability of the hosts. The novelty of the probabilistic minimum power multicast (PMPM) problem treated in this paper consists in the minimization of the assigned transmission powers, imposing at the same time a global reliability level to the solution network. An integer linear programming formulation for the PMPM problem is presented. Furthermore, an exact algorithm based on an iterative row and column generation procedure, as well as a heuristic method are proposed. Computational experiments are finally presented.     

带退货和软时间窗的多仓库选址-路径问题研究

选址-路径问题(location routing problems, LRP)是集成物流网络研究中的难题,也是任何一个大型物流配送企业必须面对的管理决策问题。本文在仓库容量约束和车辆容量约束的基础上,结合送取货一体化的配送模式和客户服务时间要求,建立了带退货和软时间窗的多仓库选址-路径(MDLRP)数学模型。针对MDLRP问题求解的复杂性,引入局部搜索算法和重组策略,设计了自适应混合遗传算法,对模型进行整体求解。最后进行数值实验,表明本文提出的模型和改进算法具有实用性和优越性,可为选址和车辆运输决策提供重要参考依据。     

Branch-and-price algorithm for a multicast routing problem

This paper considers a multicast routing problem to find the minimum cost tree where the whole communication link delay on each path(route) of the tree is subject to a given delay allowance. The problem is formulated as an integer programming problem by using path variables. An associated problem reduction property is then characterised to reduce the solution space. Moreover, a polynomial time column generation procedure is exploited to solve the associated linear programming relaxation with such solution space reduced. Therefore a branch-and-price algorithm is derived to obtain the optimal integer solution(tree) for the problem. Computational results show that the algorithm can solve practical size problems in a reasonable length of time.     

Coding and control for communication networks

The purpose of this paper is to survey techniques for constructing effective policies for controlling complex networks, and to extend these techniques to capture special features of wireless communication networks under different networking scenarios. Among the key questions addressed are:

1. The relationship between static network equilibria, and dynamic network control.
2. The effect of coding on control and delay through rate regions.
3. Routing, scheduling, and admission control.

Through several examples, ranging from multiple-access systems to network coded multicast, we demonstrate that the rate region for a coded communication network may be approximated by a simple polyhedral subset of a Euclidean space. The polyhedral structure of the rate region, determined by the coding, enables a powerful workload relaxation method that is used for addressing complexity—the relaxation technique provides approximations of a highly complex network by a far simpler one. These approximations are the basis of a specific formulation of an h-MaxWeight policy for network routing. Simulations show a 50% improvement in average delay performance as compared to methods used in current practice.     

Distribution systems design with two-level routing considerations

In this study, we formulate and analyze a strategic design model for three-echelon distribution systems with two-level routing considerations. The key design decisions considered are: the number and locations of distribution centers (DC’s), which big clients (clients with larger demand) should be included in the first level routing (the routing between plants and DC’s), the first-level routing between plants, DC’s and big clients, and the second-level routing between DC’s and other clients not included in the first-level routing. A hybrid genetic algorithm embedded with a routing heuristic is developed to efficiently find near-optimal solutions. The quality of the solution to a series of small test problems is evaluated—by comparison with the optimal solution solved using LINGO 9.0. In test problems for which exact solutions are available, the heuristic solution is within 1% of optimal. At last, the model is applied to design a national finished goods distribution system for a Taiwan label-stock manufacturer. Through the case study, we find that the inclusion of big clients in the first-level routing in the analysis leads to a better network design in terms of total logistic costs.     

AN ADAPTIVE MEMBRANE ALGORITHM FOR SOLVING COMBINATORIAL OPTIMIZATION PROBLEMS

Membrane algorithms （MAs）, which inherit from P systems, constitute a new parallel and distribute framework for approximate computation. In the paper, a membrane algorithm is proposed with the improvement that the involved parameters can be adaptively chosen. In the algorithm, some membranes can evolve dynamically during the computing process to specify the values of the requested parameters. The new algorithm is tested on a well-known combinatorial optimization problem, the travelling salesman problem. The em-pirical evidence suggests that the proposed approach is efficient and reliable when dealing with 11 benchmark instances, particularly obtaining the best of the known solutions in eight instances. Compared with the genetic algorithm, simulated annealing algorithm, neural net-work and a fine-tuned non-adaptive membrane algorithm, our algorithm performs better than them. In practice, to design the airline network that minimize the total routing cost on the CAB data with twenty-five US cities, we can quickly obtain high quality solutions using our algorithm.     

An adaptive membrane algorithm for solving combinatorial optimization problems

Membrane algorithms (MAs), which inherit from P systems, constitute a new parallel and distribute framework for approximate computation. In the paper, a membrane algorithm is proposed with the improvement that the involved parameters can be adaptively chosen. In the algorithm, some membranes can evolve dynamically during the computing process to specify the values of the requested parameters. The new algorithm is tested on a well-known combinatorial optimization problem, the travelling salesman problem. The empirical evidence suggests that the proposed approach is efficient and reliable when dealing with 11 benchmark instances, particularly obtaining the best of the known solutions in eight instances. Compared with the genetic algorithm, simulated annealing algorithm, neural network and a fine-tuned non-adaptive membrane algorithm, our algorithm performs better than them. In practice, to design the airline network that minimize the total routing cost on the CAB data with twenty-five US cities, we can quickly obtain high quality solutions using our algorithm.