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Researches on Cognitive Network Dynamic Self-adaptive Monitoring Mechanism

Author: ZhangPengCheng
Tutor: SunYanFei
School: Nanjing University of Posts and Telecommunications
Course: Information network
Keywords: Cognitive networks QoS Adaptive monitoring Situation awareness Double closed loop
CLC: TN915.09
Type: Master's thesis
Year: 2012
Downloads: 15
Quote: 0
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With rapid development of computer network technology, the current network generally exhibits features as multiple service types as well as complex and changeable network environment, which makes it hard to guarantee the end to end QoS. Limited by hierarchical structure, the traditional network can only make static and passive adjustments when QoS deteriorated and has poor adaptability to complex network situations, which, obviously, unable to meet the requirements of network development. Different from traditional network, cognitive networks have the ability of network situation perception; it can dynamically adjust network configuration in accordance with real-time network situation, therefore achieving QoS guarantee. Cognitive networks which provide new solutions for network QoS guarantee problem, have already became an important research focus of next generation network. This paper, based on the analysis of popular network QoS monitoring mechanism, mainly focus on the researches of cognitive network dynamic self-adaptive monitoring mechanism, and the specific work is shown as follows:(1) A dynamic self-monitor and self-control model for cognitive network QoS based on double closed loop and situation awareness (MCBDS) which focuses on network situation awareness and coordination of QoS control method is proposed to overcome the shortages of the current cognitive network adaptive monitoring model, such as lack of situation awareness and high control cost. Modeling and analysis by performance evaluation process algebra (PEPA) show that MCBDS is reasonable and feasible.(2) A cognitive network situation awareness method consists of knowledge awareness, real-time situation assessment and network state prediction is introduced to perceive cognitive network situation completely and provide abundant information for cognitive network QoS control. Knowledge awareness which provides knowledge acquisition and preprocessing is the base of cognitive network situation awareness. Real-time situation assessment which provides the service QoS evaluation based on the HDS and the user QoS evaluation based on QoE, describes the current situation of the network. Network situation prediction which based on SVM improved by weighted RBF kernel function (WRSVM) can judge the development of network situation. Experimental results show that the efficiency and accuracy of the cognitive network state prediction is greatly improved by WRSVM.(3) A double closed loop and dynamic self-configuration method for cognitive network QoS Based on network status (DCBS) is proposed to coordinate the various buffer management and queue scheduling algorithm, therefore making them close together to guarantee the network QoS and optimize global performance and reduce resource consumption. DCBS which aims at QoS optimization would start“maintenance ring”or“adaptive ring”according to network situation in order to guarantee network QoS meanwhile minimize the control cost so as to ensure the healthy and stabilization of network QoS. Experimental results show that DCBS can improve the health of key services, optimize the global performance, enhance the system robustness, and effectively avoid the health of sub-healthy service become worse, at the same time shorten the time of network QoS degradation and reduce the network control cost as well.

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