QoS multicast tree construction in IP/DWDM optical internet by bio-inspired algorithms

Copyright @ Elsevier Ltd. All rights reserved.In this paper, two bio-inspired Quality of Service (QoS) multicast algorithms are proposed in IP over dense wavelength division multiplexing (DWDM) optical Internet. Given a QoS multicast request and the delay interval required by the application, both algorithms are able to find a flexible QoS-based cost suboptimal routing tree. They first construct the multicast trees based on ant colony optimization and artificial immune algorithm, respectively. Then a dedicated wavelength assignment algorithm is proposed to assign wavelengths to the trees aiming to minimize the delay of the wavelength conversion. In both algorithms, multicast routing and wavelength assignment are integrated into a single process. Therefore, they can find the multicast trees on which the least wavelength conversion delay is achieved. Load balance is also considered in both algorithms. Simulation results show that these two bio-inspired algorithms can construct high performance QoS routing trees for multicast applications in IP/DWDM optical Internet.This work was supported in part ny the Program for New Century Excellent Talents in University, the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1, the National Natural Science Foundation of China under Grant no. 60673159 and 70671020, the National High-Tech Reasearch and Development Plan of China under Grant no. 2007AA041201, and the Specialized Research Fund for the Doctoral Program of Higher Education under Grant no. 20070145017

Bi-velocity discrete particle swarm optimization and its application to multicast routing problem in communication networks

This paper proposes a novel bi-velocity discrete particle swarm optimization (BVDPSO) approach and extends its application to the NP-complete multicast routing problem (MRP). The main contribution is the extension of PSO from continuous domain to the binary or discrete domain. Firstly, a novel bi-velocity strategy is developed to represent possibilities of each dimension being 1 and 0. This strategy is suitable to describe the binary characteristic of the MRP where 1 stands for a node being selected to construct the multicast tree while 0 stands for being otherwise. Secondly, BVDPSO updates the velocity and position according to the learning mechanism of the original PSO in continuous domain. This maintains the fast convergence speed and global search ability of the original PSO. Experiments are comprehensively conducted on all of the 58 instances with small, medium, and large scales in the OR-library (Operation Research Library). The results confirm that BVDPSO can obtain optimal or near-optimal solutions rapidly as it only needs to generate a few multicast trees. BVDPSO outperforms not only several state-of-the-art and recent heuristic algorithms for the MRP problems, but also algorithms based on GA, ACO, and PSO

Performance analysis for network coding using ant colony routing

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The aim of this thesis is to conduct performance investigation of a combined system of Network Coding (NC) technique with Ant-Colony (ACO) routing protocol. This research analyses the impact of several workload characteristics, on system performance. Network coding is a significant key development of information transmission and processing. Network coding enhances the performance of multicast by employing encoding operations at intermediate nodes. Two steps should realize while using network coding in multicast communication: determining appropriate transmission paths from source to multi-receivers and using the suitable coding scheme. Intermediate nodes would combine several packets and relay them as a single packet. Although network coding can make a network achieve the maximum multicast rate, it always brings additional overheads. It is necessary to minimize unneeded overhead by using an optimization technique. On other hand, Ant Colony Optimization can be transformed into useful technique that seeks imitate the ant’s behaviour in finding the shortest path to its destination using quantities of pheromone that is left by former ants as guidance, so by using the same concept of the communication network environment, shorter paths can be formulated. The simulation results show that the resultant system considerably improves the performance of the network, by combining Ant Colony Optimization with network coding. 25% improvement in the bandwidth consumption can be achieved in comparison with conventional routing protocols. Additionally simulation results indicate that the proposed algorithm can decrease the computation time of system by a factor of 20%

Multiobjective multicast routing with Ant Colony Optimization

This work presents a multiobjective algorithm for multicast traffic engineering. The proposed algorithm is a new version of MultiObjective Ant Colony System (MOACS), based on Ant Colony Optimization (ACO). The proposed MOACS simultaneously optimizes the maximum link utilization, the cost of the multicast tree, the averages delay and the maximum endtoend delay. In this way, a set of optimal solutions, known as Pareto set is calculated in only one run of the algorithm, without a priori restrictions. Experimental results obtained with the proposed MOACS were compared to a recently published Multiobjective Multicast Algorithm (MMA), showing a promising performance advantage for multicast traffic engineering.5th IFIP International Conference on Network Control & Engineering for QoS, Security and MobilityRed de Universidades con Carreras en Informática (RedUNCI

A hybrid ACO/PSO based algorithm for QoS multicast routing problem

AbstractMany Internet multicast applications such as videoconferencing, distance education, and online simulation require to send information from a source to some selected destinations. These applications have stringent Quality-of-Service (QoS) requirements that include delay, loss rate, bandwidth, and delay jitter. This leads to the problem of routing multicast traffic satisfying QoS requirements. The above mentioned problem is known as the QoS constrained multicast routing problem and is NP Complete. In this paper, we present a swarming agent based intelligent algorithm using a hybrid Ant Colony Optimization (ACO)/Particle Swarm Optimization (PSO) technique to optimize the multicast tree. The algorithm starts with generating a large amount of mobile agents in the search space. The ACO algorithm guides the agents’ movement by pheromones in the shared environment locally, and the global maximum of the attribute values are obtained through the random interaction between the agents using PSO algorithm. The performance of the proposed algorithm is evaluated through simulation. The simulation results reveal that our algorithm performs better than the existing algorithms

An efficient Particle Swarm Optimization technique for identification of shortest path in Distributed Network

No Abstrac

A new QoS Routing Architecture in NGI

After a thorough understanding of the relevant research knowledge and the key theory of NGN, I describe the research objectives and the recent development of the QoS routing in this thesis. QoS routing is regarded as the key part in the problem of the next generation of integrated-service network. A new routing algorithm is put forward in this thesis, which is better than OSPF in some aspects. As for the experiment, NS2 is chosen as the simulation environment, and some other experimental results are also included to manifest its strongpoint. The development and requirement of NGN is described in Chapter One; The definition and types of routing and the basic theories of QoS routing are described in Chapter Two; The development and research method of QoS are focused in Chapter Three. The new routing algorithm and simulation is proposed in Chapter Four

An ACO Algorithm for Effective Cluster Head Selection

This paper presents an effective algorithm for selecting cluster heads in mobile ad hoc networks using ant colony optimization. A cluster in an ad hoc network consists of a cluster head and cluster members which are at one hop away from the cluster head. The cluster head allocates the resources to its cluster members. Clustering in MANET is done to reduce the communication overhead and thereby increase the network performance. A MANET can have many clusters in it. This paper presents an algorithm which is a combination of the four main clustering schemes- the ID based clustering, connectivity based, probability based and the weighted approach. An Ant colony optimization based approach is used to minimize the number of clusters in MANET. This can also be considered as a minimum dominating set problem in graph theory. The algorithm considers various parameters like the number of nodes, the transmission range etc. Experimental results show that the proposed algorithm is an effective methodology for finding out the minimum number of cluster heads.Comment: 7 pages, 5 figures, International Journal of Advances in Information Technology (JAIT); ISSN: 1798-2340; Academy Publishers, Finlan