Cognitive Networks Oriented Self-adaptive QoS Awareness And Configuration

With the rapid development of networking technologies, its application scope has been extended every corner of the world. Specifically, network systems have become extremely complicated with the appearance of new communication technologies, which has resulted in the unreliability of network and end-to-end performances. Those current network elements could not be awareness of the action of others and the environment state due to the constraint of traditional network architecture. As a result, the adaptaion ability of network elements for complex environment is inadequate and the quality of service (QoS) can not be guaranteed. Therefore, the concept of cognitive networks (CN) is generated under this background and become an important topic for the researchers in network fields.The so called cognitive networks are the specific network systems that have ability of environment-awareness and network state-awareness. Based on awared information, the whole system targets and end-to-end targets, cognitive networks can utilize proper learning mechanism to adjust network configuration, adapt to environmental change intelligently and guide future autonomous decision-making.Cognitive networks are considered as the core and development trend of the next generation network (NGN) in commom, which now are on its infant stage, and the application complexity of cognitive network is beyond the original understanding of system designers. The end-to-end QoS problem in cognitive networks is proposed when NGN is deploied in most of the counties. How to construct the architecture and provide the effective network QoS have become the key topics in the field of cognitive networks.In this paper, we first propose a self-adaptive QoS framework of cognitve network combined with the cognitve philosophy. As a data support for next dynamic self-configuration, we then investigate the bordernet and backbone QoS data collection and analysis. Finally, QoS metrics are distinguished by utility function and the dynamic self-configuration mechanism is proposed. In this paper, we propose an approach to guarantee the end-to-end QoS in order to meet the users'need. The main contents are organized as follows.Firstly, a cognitive network QoS framework is proposed, which is ability of cross-layer awareness. Considering the demand of NGN as well as design philosophy of cognitive network QoS, a self-adaptive QoS framework is proposed which is devided into three logic layer called as"system layer-service layer-user layer"from bottom to top. QoS management elements on every layer are deployed to achive the mutual operation, and the channels are constructed to obtain the cross layer awareness and integrative QoS control. Formal analysis result shows that this proposed QoS framework meets the need of cognitive networks demand which can demonstrate philosophy of cross layer design, self-management and dynamic awareness.Secondly, according to the requirements of end-to-end QoS, a data preprocessing approach is proposed in the views of bordernet and backbone.In order to solve the bordernet QoS-awareness, a"two step"method are proposed. In the first step, DPP (Data Preprocessing) based on DS evidence theory is utilized to fusion network QoS data and classify them in the meanwhile. In the second step, the D-AdaBoost (Dynamic AdaBoost) is utilized to classify the remained QoS data by the first step.The experimental results show that our method can assure the precision of classification, eliminate the influence of uncertain noisy data and avoid the evidence conflict.In order to solve the backbone QoS-awareness, IP flow aggregation method based on median tree is proposed in this paper. By analyzing the IP flow statistical characteristics of backbone networks, the propoed method has a capability of analyzing backbone IP flows in the views of"cluster"and"combination". The simulation experiments show that IP flow aggregation method can handle the large scale data effectively offline. Moreover, our method can demonstrate IP flow characteristics of backbone networks accurately by statistics characteristics combination.Lastly, we propose a QoS dynamic self-configuration method based on utility function. Taking cognitive characteristics and self-management ability into consideration, the utility function is used to distinguish QoS priority and adopt interruption policy to dynamically modify the QoS priority which can reinforce the users'QoS support when the network is congested. The experiment results show that our method can alleviate the congestiong on the bottleneck links to some extent, reduce the data packets lost and maintain the user satisfaction when bottleneck links are congested.