Huanlai Xing

A compact genetic algorithm for the network coding based resource minimization problem

In network coding based data transmission, intermediate nodes in the network are allowed to perform mathematical operations to recombine (code) data packets received from different incoming links. Such coding operations incur additional computational overhead and consume public resources such as buffering and computational resource within the network. Therefore, the amount of coding operations is expected to be minimized so that more public resources are left for other network applications.In this paper, we investigate the newly emerged problem of minimizing the amount of coding operations required in network coding based multicast. To this end, we develop the first elitism-based compact genetic algorithm (cGA) to the problem concerned, with three extensions to improve the algorithm performance. First, we make use of an all-one vector to guide the probability vector (PV) in cGA towards feasible individuals. Second, we embed a PV restart scheme into the cGA where the PV is reset to a previously recorded value when no improvement can be obtained within a given number of consecutive generations. Third, we design a problem-specific local search operator that improves each feasible solution obtained by the cGA. Experimental results demonstrate that all the adopted improvement schemes contribute to an enhanced performance of our cGA. In addition, the proposed cGA is superior to some existing evolutionary algorithms in terms of both exploration and exploitation simultaneously in reduced computational time.

A multi-granularity evolution based Quantum Genetic Algorithm for QoS multicast routing problem in WDM networks

QoS multicast routing problem in WDM networks is investigated, and an improved algorithm Multi-granularity Evolution based Quantum Genetic Algorithm (MEQGA) is proposed to address it. Based on Quantum Genetic Algorithm (QGA) with quantum rotation gate strategy, MEQGA introduces multi-granularity evolution mechanism, which allows different chromosomes of one generation to have different rotation angle step values to update. In term of this mechanism, MEQGA can significantly improve its capability of exploration and exploitation, since its optimization performance does not over-depend on the single rotation angle step scheme shared by all chromosomes any longer. MEQGA also presents an adaptive quantum mutation operation which is able to avoid local search efficiently. A repair method is applied to eliminate illegal graphs as many as possible hence more excellent solutions will appear in each evolutionary generation. Simulation results show that, for the QoS multicast routing problem, MEQGA outperforms other heuristic algorithms and is characterized by robustness, high success ratio, fast convergence and excellent capability on global searching.

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.

A Modified Ant Colony Optimization Algorithm for Network Coding Resource Minimization

This paper presents a modified ant colony optimization (ACO) approach for the network coding resource minimization problem. It is featured with several attractive mechanisms specially devised for solving the concerned problem: 1) a multidimensional pheromone maintenance mechanism is put forward to address the issue of pheromone overlapping; 2) problem-specific heuristic information is employed to enhance the capability of heuristic search (neighboring area search); 3) a tabu-table-based path construction method is devised to facilitate the construction of feasible (link-disjoint) paths from the source to each receiver; 4) a local pheromone updating rule is developed to guide ants to construct appropriate promising paths; and 5) a solution reconstruction method is presented, with the aim of avoiding prematurity and improving the global search efficiency of proposed algorithm. Due to the way it works, the ACO can well exploit the global and local information of routing-related problems during the solution construction phase. The simulation results on benchmark instances demonstrate that with the integrated five extended mechanisms, our algorithm outperforms a number of existing algorithms with respect to the best solutions obtained and the computational time.

An adaptive-evolution-based quantum-inspired evolutionary algorithm for QoS multicasting in IP/DWDM networks

This paper investigates least-cost QoS multicast routing problem in IP/DWDM optical networks, and proposes an improved evolutionary algorithm (AEQEA). Based on quantum-inspired evolutionary algorithm (QEA) with quantum rotation gate strategy, AEQEA introduces adaptive evolution mechanism (AEM), which allows each chromosome in a population to update itself to a fitter position according to its own situation. In term of this mechanism, AEQEA can significantly improve its capability of exploration and exploitation, since every chromosome is able to be allocated with suitable evolutionary parameters before each update. A repair method is applied to eliminate illegal graphs as many as possible hence more excellent solutions will appear in each evolutionary generation. Simulations are carried out over a number of network topologies. And the results show that, for the QoS multicasting problem, AEQEA outperforms other existing heuristic algorithms and is characterized by robustness, high success ratio, fast convergence and excellent capability on global searching.

SD-Anti-DDoS

In order to overcome Distributed Denial of Service (DDoS) in Software Defined Networking (SDN), this paper proposes a mechanism consisting of four modules, namely attack detection trigger, attack detection, attack traceback and attack mitigation. The trigger of attack detection mechanism is introduced for the first time to respond more quickly against DDoS attack and reduce the workload of controllers and switches. In the meantime, the DDoS attack detection method based on neural network is implemented to detect attack. Furthermore, an attack traceback method taking advantages of the characteristics of SDN is also proposed. Meanwhile, a DDoS mitigation mechanism including attack blocking and flow table cleaning is presented. The proposed mechanism is evaluated on SDN testbed. Experimental results show that the proposed mechanism can quickly initiate the attack detection with less than one second and accurately trace the attack source. More importantly, it can block the attack in source and release the occupied resources of switches.

A Population Based Incremental Learning for Network Coding Resources Minimization

In network coding based multicast, coding operations need to be minimized as they consume computational resources and increase data processing complexity at corresponding nodes in the network. To address the problem, we develop a population based incremental learning algorithm which shows to outperform existing algorithms in terms of both the solution obtained and computational time consumed on networks with various features.

Self-optimised Coordinated Traffic Shifting Scheme for LTE Cellular Systems

Mobility load balancing is widely used in LTE cellular systems to deal with the uneven load distribution. Its basic idea is to shift traffic from a hot-spot cell to less-loaded neighbouring cells, called partners. Conventional schemes focus on the hot-spot cell’s load reduction and pay less attention to the performance of partners. This paper proposes a self-optimised coordinated traffic shifting scheme. In the proposed scheme, the coordination among partners is considered. Meanwhile, the shifted traffic is adjusted dynamically according to the load balancing (LB) performance. Simulation results show the proposed scheme can keep low call blocking probability of partners. It can also keep the number of Ping-Pong LB and the LB handover dropping probability at low levels.

Self-optimised joint traffic offloading in heterogeneous cellular networks

Traffic offloading is a widely used technique to address the unbalanced traffic distribution between pico cells and macro cells in heterogeneous cellular networks. However, the shifted users may result in the macro cell receiving large traffic from multiple pico cells and then becoming heavily loaded. This phenomenon is called the exacerbation problem in this paper. In order to address this problem and balance load effectively, this paper proposes a self-optimised joint traffic offloading (JTO) scheme. The JTO scheme jointly employs two traffic offloading techniques, including cell biasing technique to offload traffic between pico cell and macro cell, and mobility load balancing among macro cells. Simulation results show the JTO scheme can effectively deal with the cell exacerbation problem, in terms of handover failure and call dropping. The JTO scheme can also reduce the call blocking probability.

A population based incremental learning for delay constrained network coding resource minimization

In network coding based multicast, coding operations are expected to be minimized as they not only incur additional computational cost at corresponding nodes in network but also increase data transmission delay. On the other hand, delay constraint must be concerned particularly in delay sensitive applications, e.g. video conferencing. In this paper, we study the problem of minimizing the amount of coding operations required while meeting the end-to-end delay constraint in network coding based multicast. A population based incremental learning (PBIL) algorithm is developed, where a group of best so far individuals, rather than a single one, is maintained and used to update the probability vector, which enhances the global search capability of the algorithm. Simulation results demonstrate the effectiveness of our PBIL.