Xingwei Wang

QoS multicast routing for multimedia group communications using intelligent computational methods

In this paper, we investigate the problem of quality-of-service (QoS) multicast routing for multimedia group communications. We first develop a unified framework for achieving QoS multicast trees using intelligent computational methods. The framework consists of the model for multimedia communication network, the formulation of QoS multicast routing problem, and three key components used in intelligent computational methods-based QoS multicast routing algorithms. Then we propose three QoS multicast algorithms based on three representative intelligent computational methods (i.e., genetic algorithm, simulated annealing, and Tabu search), separately. In these algorithms, both the network resource requirements and the end-to-end delay are considered as the QoS parameters. Various issues are analyzed and designed for applying these three intelligent computational methods to construct QoS multicast trees. By simulation, we evaluate the performance of these three algorithms on a small-scale real-world multimedia communication network and a randomly generated large-scale network. Simulation results show that our algorithms can find near-optimal QoS multicast trees with high success rate. We also compare the running time among them, which help explain the algorithmic structures.

Rate and Power Adaptation for Analog Network Coding

Network coding is an emerging technique of packet forwarding that encodes the packets at the relay node to increase the throughput of a relaying network. Analog network coding (ANC) encodes the packets by superposing the signals at the physical layer, which can further bring throughput improvements. In this paper, we first analyze the coding procedure of ANC and elaborate the fact that the maximum possible number of coding nodes in ANC is two for common wireless transceivers. Then, we propose a rate adaptation scheme for ANC. We show that the transmission powers of nodes affect the data rate and that transmitting at the maximum power is not always optimal. Based on this observation, we propose a method of finding the optimal transmission power that maximizes the data rate. We also discuss some issues that need to be considered when implementing the proposed scheme in practical wireless systems. Then, the performance of the proposed scheme is evaluated through extensive simulations. The simulation results show that the proposed joint rate and power adaptation scheme outperforms ANC without power adaptation and that it is beneficial over other relaying schemes in a broad range of scenarios of mobile and vehicular applications.

Distributed MAC Protocol Supporting Physical-Layer Network Coding

Physical-layer network coding (PNC) is a promising approach for wireless networks. It allows nodes to transmit simultaneously. Due to the difficulties of scheduling simultaneous transmissions, existing works on PNC are based on simplified medium access control (MAC) protocols, which are not applicable to general multihop wireless networks, to the best of our knowledge. In this paper, we propose a distributed MAC protocol that supports PNC in multihop wireless networks. The proposed MAC protocol is based on the carrier sense multiple access (CSMA) strategy and can be regarded as an extension to the IEEE 802.11 MAC protocol. In the proposed protocol, each node collects information on the queue status of its neighboring nodes. When a node finds that there is an opportunity for some of its neighbors to perform PNC, it notifies its corresponding neighboring nodes and initiates the process of packet exchange using PNC, with the node itself as a relay. During the packet exchange process, the relay also works as a coordinator which coordinates the transmission of source nodes. Meanwhile, the proposed protocol is compatible with conventional network coding and conventional transmission schemes. Simulation results show that the proposed protocol is advantageous in various scenarios of wireless applications.

A cross-layer optimization based integrated routing and grooming algorithm for green multi-granularity transport networks

With the development of IP networks and intelligent optical switch networks, the backbone network tends to be a multi-granularity transport one. In a multi-granularity transport network (MTN), due to the rapid growth of various applications, the scale and complexity of network devices are significantly enhanced. Meanwhile, to deal with bursty IP traffic, the network devices need to provide continuous services along with excessive power consumption. It has attracted wide attention from both academic and industrial communities to build a power-efficient MTN. In this paper, we design an effective node structure for MTN. Considering the power savings on both IP and optical transport layers, we propose a mathematical model to achieve a cross-layer optimization objective for power-efficient MTN. Since this optimization problem is NP-hard (Hasan et al. (2010) [11]) and heuristic or intelligent optimization algorithms have been successfully applied to solve such kinds of problems in many engineering domains (Huang et al. (2011) [13], Li et al. (2011) [17] and Dong et al. (2011) [5]), a Green integrated Routing and Grooming algorithm based on Biogeography-Based Optimization (Simon (2008) [23]) (GRG_BBO) is also presented. The simulation results demonstrate that, compared with the other BBO based and state-of-the-art power saving approaches, GRG_BBO improves the power savings at a rate between 2%-15% whilst the high-level multi-user QoS (Quality of Services) satisfaction degree (MQSD) is guaranteed. GRG_BBO is therefore an effective technique to build a power-efficient MTN.

A Resource Auction Based Allocation Mechanism in the Cloud Computing Environment

In this paper, certain microeconomic methods are introduced to solve the problem of management and allocation of resources in the cloud environment. By adding the idea of batch matching into the reverse auction, a reverse batch matching auction mechanism is proposed. Based on the auction, the strategy of twice-punishment and the pursuit of service quality can effectively prevent the occurrence of trading fraud. Three evaluation criteria, including the market efficiency, user satisfaction and service quality, are used as the optimization objectives, and the immune evolutionary algorithm is applied to find the optimal resource allocation solution. The simulation results have shown that the proposed mechanism is both feasible and effective.

A multipopulation parallel genetic simulated annealing-based QoS routing and wavelength assignment integration algorithm for multicast in optical networks

In this paper, we propose an integrated Quality of Service (QoS) routing algorithm for optical networks. Given a QoS multicast request and the delay interval specified by users, the proposed algorithm can find a flexible-QoS-based cost suboptimal routing tree. The algorithm first constructs the multicast tree based on the multipopulation parallel genetic simulated annealing algorithm, and then assigns wavelengths to the tree based on the wavelength graph. In the algorithm, routing and wavelength assignment are integrated into a single process. For routing, the objective is to find a cost suboptimal multicast tree. For wavelength assignment, the objective is to minimize the delay of the multicast tree, which is achieved by minimizing the number of wavelength conversion. Thus both the cost of multicast tree and the user QoS satisfaction degree can approach the optimal. Our algorithm also considers load balance. Simulation results show that the proposed algorithm is feasible and effective. We also discuss the practical realization mechanisms of the algorithm.

An Intelligent Economic Approach for Dynamic Resource Allocation in Cloud Services

With Inter-Cloud, distributed cloud and open cloud exchange (OCX) emerging, a comprehensive resource allocation approach is fundamental to highly competitive cloud market. Oriented to infrastructure as a service (IaaS), an intelligent economic approach for dynamic resource allocation (IEDA) is proposed with the improved combinatorial double auction protocol devised to enable various kinds of resources traded among multiple consumers and multiple providers at the same time enable task partitioning among multiple providers. To make bidding and asking reasonable in each round of the auction and determine eligible transaction relationship among providers and consumers, a price formation mechanism is proposed, which is consisted of a back propagation neural network (BPNN) based price prediction algorithm and a price matching algorithm. A reputation system is proposed and integrated to exclude dishonest participants from the cloud market. The winner determination problem (WDP) is solved by the improved paddy field algorithm (PFA). Simulation results have shown that IEDA can not only help maximize market surplus and surplus strength but also encourage participants to be honest.

Routing security scheme based on reputation evaluation in hierarchical ad hoc networks

In this paper, we propose a new Routing Security Scheme based on Reputation Evaluation (RSSRE) to meet security requirements in hierarchical ad hoc networks. In this model, the reputation relationship is defined in consideration of the related node roles and functions, while the reputation evaluation mechanism is built based on the correlation among nodes that need to be evaluated. The dynamic reputation threshold is used to improve routing security with the precondition of usability. The reputation information of nodes is updated with different roles. We can reconstruct the route to solve attack problems in transmitting packets. Simulation results show that compared with traditional reputation evaluation models, the proposed model in this paper can more timely and accurately reflect security status and execute improved routing when there are malicious nodes in hierarchical Ad Hoc networks.

A resource allocation method based on the limited English combinatorial auction under cloud computing environment

On account of the resource characteristics under cloud computing environment and the flexibility and availability of applying economic mechanism to resource allocation, a resource allocation model based on the limited English combinatorial auction under cloud computing environment is advanced. An improved periodical auction model is constructed, and then ribbon capacity is adopted to describe the special storage capacity for special users. Based on the above, the resource trading price between resource buyers and resource providers is determined based on the limited English combinatorial auction model. In the end, the optimal resource allocation solution is pursued based on genetic algorithm. Simulation results have shown that the proposed model is both feasible and effective, which can maximize the seller total trading amounts as well as reduce the executing time of winner determination.

New insights on survivability in multi-domain optical networks

With the development of intelligent optical networks and the general multi-protocol label switching (GMPLS) technique, the seamless convergence between IP network and optical network is no longer be a dream but a practical reality. Similar to the Internet, current optical networks have been divided into multiple domains each of which has its own network provider and management policy. Therefore, the development of multi-domain optical networks will be the trend of new-generation intelligent optical networks, and GMPLS-based survivability for multi-domain optical networks will become a hot topic of research in the future. This paper provides a comprehensive review of the existing survivable schemes in multi-domain optical networks and analyzes the shortcomings of current research. Based on previous studies, we present possible challenges and propose new ideas to design efficient survivable schemes to guide the future work of researchers in multi-domain optical networks.