基于贝叶斯网络的认知网络QoS自主控制技术研究
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摘要
互联网发展至今，已经成为一个庞大的非线性动态多变的复杂系统，随着网络接入技术的日趋多样化和网络承载业务的不断丰富，传统的网络QoS管理和控制方式面临着诸多挑战。由于网络不具备自主性和智能性，网络对自己的状态和行为缺乏全面认知，网络日益复杂使得传统的孤岛式、静态式QoS保证方法表现低效且决策反应被动，网络经常拥塞，QoS性能时常劣化，业务传输与QoS保证不能根据环境的变化动态调整，全网资源利用效率低下，导致用户QoS满意度变差。认知网络是受认知无线电理论和技术的启发而提出的一种具有认知特征的主动网络，认知网络能够感知网络整体状态，据此进行计划和决策，并执行相应的动作，具有推理和学习的能力，被认为是以无线、移动、宽带和全IP化为基本特征的未来通信网络发展的必然趋势。
本论文所研究的认知网络QoS自主控制技术，主要是指在认知网络环境下实施QoS决策和控制时所采取的具有自主性和智能性的方法，解决动态多变网络适变性差、全网效能低的问题，有效提高网络资源利用率，保证网络端到端的QoS。针对当前网络“多业务、需求差异、动态时变、资源稀缺”等问题，主要从面向业务的认知网络QoS自主控制框架、认知网络QoS健康度评估、认知网络QoS劣化定位和认知网络QoS自主控制等角度，借助贝叶斯网络的理论与方法，深入研究认知网络环境下面向业务的QoS自主控制机制，实现复杂多变认知网络环境下的全局性可监测、可分析、可控制，提高网络资源利用率，保证网络端到端QoS。主要研究内容概括如下：
1.提出了一种面向业务的认知网络QoS自主控制框架。以认知网络业务端到端QoS需求为出发点，结合认知网络QoS自主控制设计目标，在分析现有典型QoS控制框架优点与不足的基础之上，结合OODA的认知过程，提出了一种面向业务的认知网络QoS自主控制框架。
2.提出了一种基于模糊动态贝叶斯网络的认知网络QoS健康度评估方法。从认知网络QoS健康度评估宏观角度出发，考虑到网络中尽可能多的网元、业务及链路，重新定义了时延、抖动、丢包率三个QoS参数，并建立了认知网络QoS健康度评估的动态贝叶斯网络模型。利用模糊分类方法，将连续变量模糊分类为DBN能够应用的证据信息进行学习和推理，应用直接推理算法进行推理，从而得到连续时间片上的认知网络QoS健康度概率及其发展趋势，为网络决策和控制提供了具有参考价值的评估结果。
3.提出了一种基于贝叶斯网络的认知网络QoS劣化定位方法。考虑到端到端的探测方法存在探测代价高和无法精确定位的局限性，建立一种基于贝叶斯网络的认知网络QoS劣化定位模型，利用LM1丢包率模型通过少量的端到端探测获取路径状态信息，按照贝叶斯估计方法进行链路劣化的先验概率学习。然后根据获得的证据节点状态信息，将无关劣化的部分路径及其该路径覆盖的链路从模型中删除来降低模型复杂度，然后采用局部联合树算法推理，最终实现对QoS劣化链路的精确定位，为业务等级的QoS自主控制提供准确依据。
4.提出了一种基于影响图的认知网络QoS自主控制方法。结合传统网络和认知网络领域的相关知识，构建了面向业务的认知网络QoS自主控制的影响图模型，利用贝叶斯网络的簇树推理算法和结点实例化的最优决策方法实现认知网络的自学习和推理，并通过影响图，执行使得效用方程最大化的动作，实现认知网络QoS的自主决策与控制。
The Internet has developed into a huge and complex system with the characteristics ofnonlinear and dynamic. Meanwhile, QoS management and control in the traditional networkis undergoing many challengeswith the new network access technologies and continuousenrichment of network bearing service. Due to the lack of autonomy and intelligence, thenetwork does not have a comprehensive knowledge of its own conditions and behaviors.And the increasingly complex network contributes to the results that the traditional QoSguarantee method of isolated and static performs inefficient and makes decisions passively.Then the network is often congested and its QoS performance becomes deteriorated. Whenservice transmission and QoS guarantee cannot be dynamically adjusted according tochanges in the environment, both network resource utilization and User Satisfaction Degreewill be degraded. Cognitive network is proposed as an active network with the characteristicof cognitive with the inspiration of the technology of cognitive radio. A cognitive network isa network with cognitive process that can perceive current network conditions. It can plan,decide and act on those conditions. It is considered to be the inevitable trend of futurecommunication network with fundamental features of wireless, mobile, broadband andall-IP.
Autonomous control technology of QoS for cognitive network studied in this thesismainly refers to the autonomous, intelligent and self-adaptive methods during theimplementation of Qos decision and control in the cognitive environment. It can ensureend-to-end QoS of network and promote the resource utility. According to the problemssuch as multi-service, demand differences, dynamic change, resource scarcity in currentnetwork, several issues are taken into consideration in the paper, such as QoS health degreeassessment, QoS degradation location and QoS autonomous control, etc. Bayesian networktheory is used to realize global monitoring, analyzability, control ability in the dynamic andcomplex cognitive network environment.
1. A service-oriented cognitive network QoS control framework is proposed in thispaper. We first research end-to-end QoS requirements of cognitive network service aimingat cognitive network QoS autonomous control. A service-oriented cognitive network QoScontrol framework is provided combining with OODA process based on the analysis of thegeneral QoS framework.
2. A QoS health degree assessment method based on Fuzzy Dynamic BayesianNetwork is proposed. From the macroscopic point, with the consideration of differentnetwork elements, services and links of the network, re-define the three QoS parameters-delay, jitter, packet loss rate, and build the dynamic Bayesian network model ofcognitive network QoS health degree assessment. Using fuzzy classification method, thecontinuous variable fuzzy classification DBN evidence can be applied to the learning andreasoning, reasoning by directly reasoning algorithm, getting the cognitive network QoShealthy degree of probability and its development trend in continuous time slices.Theseassessment results provide a reference value for the network decision and control.
3. A deterioration location method based on Bayesian Network is established forcognitive network, which allows for the high cost in end-to-end probe method andlimitation of imprecise positioning. Path status information is obtained through a smallnumber of end-to-end probing in LM1packet loss rate model. A link degradation prioriprobability learning for link failure is carried out in accordance with the Bayesianestimation method. On this base, part of paths that have nothing to do with degradation andthe relative links these paths cover are deleted from model, which can degrade thecomplexity of the model. A precise positioning of QoS degraded links is achieved throughreasoning of local joint tree algorithm, which achieves accurate foundation for QoSindependent control of traffic level.
4. An autonomous control method of QoS for cognitive network based on influencediagramis proposed. Combing with related knowledge of traditional network and cognitivenetwork, it analyses and determines the influencing factors of service-oriented autonomouscontrol of QoS for cognitive network, constructs the corresponding influence diagrammodel. Ultimately the autonomous control methodof QoS for cognitive network based on akind of influence diagram can be realized. This method achieves self-learning andself-reasoning with Bayesian network cluster tree reasoning algorithm and optimal decisionmethod of nodes instantiation. It realize independent decision and control of the QoS forcognitive network through influence diagram and maximizing the utility equation.
本论文所研究的认知网络QoS自主控制技术，主要是指在认知网络环境下实施QoS决策和控制时所采取的具有自主性和智能性的方法，解决动态多变网络适变性差、全网效能低的问题，有效提高网络资源利用率，保证网络端到端的QoS。针对当前网络“多业务、需求差异、动态时变、资源稀缺”等问题，主要从面向业务的认知网络QoS自主控制框架、认知网络QoS健康度评估、认知网络QoS劣化定位和认知网络QoS自主控制等角度，借助贝叶斯网络的理论与方法，深入研究认知网络环境下面向业务的QoS自主控制机制，实现复杂多变认知网络环境下的全局性可监测、可分析、可控制，提高网络资源利用率，保证网络端到端QoS。主要研究内容概括如下：
1.提出了一种面向业务的认知网络QoS自主控制框架。以认知网络业务端到端QoS需求为出发点，结合认知网络QoS自主控制设计目标，在分析现有典型QoS控制框架优点与不足的基础之上，结合OODA的认知过程，提出了一种面向业务的认知网络QoS自主控制框架。
2.提出了一种基于模糊动态贝叶斯网络的认知网络QoS健康度评估方法。从认知网络QoS健康度评估宏观角度出发，考虑到网络中尽可能多的网元、业务及链路，重新定义了时延、抖动、丢包率三个QoS参数，并建立了认知网络QoS健康度评估的动态贝叶斯网络模型。利用模糊分类方法，将连续变量模糊分类为DBN能够应用的证据信息进行学习和推理，应用直接推理算法进行推理，从而得到连续时间片上的认知网络QoS健康度概率及其发展趋势，为网络决策和控制提供了具有参考价值的评估结果。
3.提出了一种基于贝叶斯网络的认知网络QoS劣化定位方法。考虑到端到端的探测方法存在探测代价高和无法精确定位的局限性，建立一种基于贝叶斯网络的认知网络QoS劣化定位模型，利用LM1丢包率模型通过少量的端到端探测获取路径状态信息，按照贝叶斯估计方法进行链路劣化的先验概率学习。然后根据获得的证据节点状态信息，将无关劣化的部分路径及其该路径覆盖的链路从模型中删除来降低模型复杂度，然后采用局部联合树算法推理，最终实现对QoS劣化链路的精确定位，为业务等级的QoS自主控制提供准确依据。
4.提出了一种基于影响图的认知网络QoS自主控制方法。结合传统网络和认知网络领域的相关知识，构建了面向业务的认知网络QoS自主控制的影响图模型，利用贝叶斯网络的簇树推理算法和结点实例化的最优决策方法实现认知网络的自学习和推理，并通过影响图，执行使得效用方程最大化的动作，实现认知网络QoS的自主决策与控制。
The Internet has developed into a huge and complex system with the characteristics ofnonlinear and dynamic. Meanwhile, QoS management and control in the traditional networkis undergoing many challengeswith the new network access technologies and continuousenrichment of network bearing service. Due to the lack of autonomy and intelligence, thenetwork does not have a comprehensive knowledge of its own conditions and behaviors.And the increasingly complex network contributes to the results that the traditional QoSguarantee method of isolated and static performs inefficient and makes decisions passively.Then the network is often congested and its QoS performance becomes deteriorated. Whenservice transmission and QoS guarantee cannot be dynamically adjusted according tochanges in the environment, both network resource utilization and User Satisfaction Degreewill be degraded. Cognitive network is proposed as an active network with the characteristicof cognitive with the inspiration of the technology of cognitive radio. A cognitive network isa network with cognitive process that can perceive current network conditions. It can plan,decide and act on those conditions. It is considered to be the inevitable trend of futurecommunication network with fundamental features of wireless, mobile, broadband andall-IP.
Autonomous control technology of QoS for cognitive network studied in this thesismainly refers to the autonomous, intelligent and self-adaptive methods during theimplementation of Qos decision and control in the cognitive environment. It can ensureend-to-end QoS of network and promote the resource utility. According to the problemssuch as multi-service, demand differences, dynamic change, resource scarcity in currentnetwork, several issues are taken into consideration in the paper, such as QoS health degreeassessment, QoS degradation location and QoS autonomous control, etc. Bayesian networktheory is used to realize global monitoring, analyzability, control ability in the dynamic andcomplex cognitive network environment.
1. A service-oriented cognitive network QoS control framework is proposed in thispaper. We first research end-to-end QoS requirements of cognitive network service aimingat cognitive network QoS autonomous control. A service-oriented cognitive network QoScontrol framework is provided combining with OODA process based on the analysis of thegeneral QoS framework.
2. A QoS health degree assessment method based on Fuzzy Dynamic BayesianNetwork is proposed. From the macroscopic point, with the consideration of differentnetwork elements, services and links of the network, re-define the three QoS parameters-delay, jitter, packet loss rate, and build the dynamic Bayesian network model ofcognitive network QoS health degree assessment. Using fuzzy classification method, thecontinuous variable fuzzy classification DBN evidence can be applied to the learning andreasoning, reasoning by directly reasoning algorithm, getting the cognitive network QoShealthy degree of probability and its development trend in continuous time slices.Theseassessment results provide a reference value for the network decision and control.
3. A deterioration location method based on Bayesian Network is established forcognitive network, which allows for the high cost in end-to-end probe method andlimitation of imprecise positioning. Path status information is obtained through a smallnumber of end-to-end probing in LM1packet loss rate model. A link degradation prioriprobability learning for link failure is carried out in accordance with the Bayesianestimation method. On this base, part of paths that have nothing to do with degradation andthe relative links these paths cover are deleted from model, which can degrade thecomplexity of the model. A precise positioning of QoS degraded links is achieved throughreasoning of local joint tree algorithm, which achieves accurate foundation for QoSindependent control of traffic level.
4. An autonomous control method of QoS for cognitive network based on influencediagramis proposed. Combing with related knowledge of traditional network and cognitivenetwork, it analyses and determines the influencing factors of service-oriented autonomouscontrol of QoS for cognitive network, constructs the corresponding influence diagrammodel. Ultimately the autonomous control methodof QoS for cognitive network based on akind of influence diagram can be realized. This method achieves self-learning andself-reasoning with Bayesian network cluster tree reasoning algorithm and optimal decisionmethod of nodes instantiation. It realize independent decision and control of the QoS forcognitive network through influence diagram and maximizing the utility equation.
引文
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