Xuesong Qiu

QoE/QoS driven simulated annealing-based genetic algorithm for Web services selection

Abstract In order to improve the efficiency and effect of Web service selection, an algorithm named quality of experience (QoE)/quality of service (QoS) driven simulated annealing-based genetic algorithm (QQDSGA) is given to achieve Web services selection efficiently with excellent QoE. QQDSGA includes two parts: QoE/QoS driven composite Web services evaluation model and simulated annealing-based (SA-based) genetic algorithm for Web services selection. Simulations show that when used in Web service selection, QQDSGA is better than genetic algorithm (GA) and SA in efficiency and is more satisfied in effect than no customer feedback.

A Survivable Virtual Network Embedding scheme based on load balancing and reconfiguration

Network virtualization has been regarded as a core attribute of the Future Internet. In a Network Virtualization Environment (NVE), heterogeneous virtual networks can share the same physical infrastructure regardless of their different topologies, demands, protocols and so on. In this case, the Survivable Virtual Network Embedding (SVNE) problem becomes increasingly critical to overcome the failure of physical infrastructure. Backup resources needed to provide survivability of virtual network undoubtedly increase the challenge of resources efficiency of SVNE. In this paper, we study the SVNE problem and propose a method of allocating bandwidth resources based on load balancing of the physical resources and a strategy of reconfiguring backup resources. Simulation experiments show that load balancing based method has a higher performance in the long term acceptance ratio, revenues and utilization of substrate links. And the reconfiguration of backup resources is cost-efficient and also helpful to increase the acceptance ratio.

A flow-based anomaly detection method using sketch and combinations of traffic features

With the development of high-speed networks, the challenge of effectively analyzing the massive data source for anomaly detection and diagnosis is yet to be resolved. This paper proposes a new flow-based anomaly detection method based on summary data structures and combinations of traffic features. Using IPFIX flow records as input, parallel sketches are established for chosen traffic features respectively. For each sketch, we use Holt-Winters forecasting technique to achieve their forecast sketches and deviation matrixes. When the deviation exceeds a certain threshold, sub-alarms will be generated. According to the characteristics of various attacks and combinations of traffic features, sub-alarms can be merged into final alarms. While sketches of flows are being constructed, destination addresses are recorded in linked lists which are used to locate victims by a series of set operations. This method can not only detect the existence of anomalies in near real time, but can roughly indicate the anomaly types and locate abnormal addresses.

A flow-based anomaly detection method using entropy and multiple traffic features

Network traffic anomaly detection is an important component in network security and management domains which can help to improve availability and reliability of networks. This paper proposes a flow-based anomaly detection method with the help of entropy. Using IPFIX, flow records containing multiple traffic features are collected in each time window. With entropy, joint probability space for multiple traffic features is constructed which is the basis of the proposed scheme. The anomaly detection method is composed of two stages. The first stage is to systematically construct the probability distribution of traffic features in normal traffic pattern. In the second stage, to detect abnormal network activities, the improved Kullback-Leibler distance between the observed probability distribution for the multiple traffic features and the forecast distribution which can be achieved by Holt-Winters technique is calculated. The improved Kullback-Leibler distance is a calculation that measures the level of difference of two probability distributions. When the distance exceeds a pre-set threshold, alerts will be generated. Finally, the scheme is demonstrated by experiment and the result shows that this method has high accuracy and low complexity.

A cluster-based negotiation model for task allocation in Wireless Sensor Network

This paper studies task allocation in cluster-based Wireless Sensor Network (WSN) using negotiation model. We study both the negotiation reasoning model and the negotiation protocol for task allocation to achieve energy efficiency while balancing nodes energy. Reasoning model determines the offer generate scheme and gives control over negotiation process. A time depending Boulware function is used as the concession strategy in reasoning model to balancing efficiency and utility. Contract net protocol is used as negotiation protocol to regulate the interaction style of nodes. The goals of this study are: 1) energy efficiency task allocation; 2) maintaining energy balance of nodes in WSN after the task to prolong the network life cycle. Experimental results using this cluster-based negotiation model task allocation approach verify its performance.

A management process defining approach for converged services based on eTOM and ITIL

Present operating management methods can not satisfy the emerging requirements along with the rapid development of the converged services. A novel management process defining approach for the converged services is presented, which aims at the management of the converged services. The approach proposes the convergence of eTOM and ITIL to build the management process framework, which is based on the standard process framework eTOM and refined by ITIL best practice for exerting their respective strength of two frameworks in the areas of the management process. The approach is verified through defining Incident Management process, Change Management process and Customer Order Handling process, and provides the basis for defining the other management processes.

Multi-task overlapping coalition formation mechanism in wireless sensor network

Coalition formation is an essential component for in wireless sensor network (WSN). Most of current coalition formation algorithms have focused on disjoint coalitions. We develop an improved ant colony algorithm to solve the overlapping coalition formation(OCF) problem in multiple coalitions in WSN domain. In this improved ant colony algorithm, we bring in mutation operation and elite strategy from genic algorithm. By doing this, it will improve the pheromone update strategy and allow sensors to allocate different parts of their resources to serve different coalitions simultaneously.