基于智能策略的网络自管理模型及应用研究
|
摘要
随着计算机网络向大型、异构、高速的方向急剧发展,网络管理变得越来越重要。与此同时,网络飞速发展使网络管理的复杂程度越来越高,威胁网络安全的因素层出不穷。致使网络管理员面对如此复杂的网络显得束手无策,付出大量的工作却错误百出。为了减轻操作的复杂性,实现系统的自主运作与智能管理,受到生物自主神经系统的启发,自主计算的构想被提出,希望能够像生物一样实现系统的智能化管理。因此,自主计算的最终目标是实现系统的自管理,而这里的自管理则是指使系统具有自配置、自愈、自优化、自保护的基本特性。
基于策略的网络管理作为自主计算研究的一个分支,它使得管理员能够从网络整体出发,以全局的观念进行策略配置,从而提高网络管理的可扩展性和灵活性,以实现网络管理的智能化。因此,策略管理在新一代网络管理研究中占据举足轻重的地位。然而,如何满足自主计算提出的要求,尤其是策略的自管理、动态自配置、自优化和安全可靠性等方面成为有待解决的关键性问题。
本论文正是针对以上问题,受生物智能中条件反射独特机理的启发,提出了基于条件反射的智能策略描述方法,并构建基于智能策略的网络自管理模型,以实现网络策略的自管理。为了更快地将策略管理推广到现有的网络管理中,本论文还研究了一个结合传统SNMP网络管理和策略管理的网管模型。此外,为了使策略能随环境自行改变调整,我们引入了策略优化方法以及策略休眠机制。最后,针对校园网中存在的各种不安全因素,运用相应的安全策略与管理模型,以增强校园网的可控性和安全性。本文的主要研究成果与特色如下:
(1)分析了反射及条件反射与策略管理的相似性,将反射及条件反射过程用策略进行描述,特别是将巴浦洛夫条件反射的各种经典实验用策略进行描述。随后相对应地给出一些与条件反射类似的网络安全案例,并对这些网络安全管理过程以条件反射学习策略的方式描述。从而将策略的动态自适应选择以条件反射的学习过程表示:当条件发生变化时,实现策略的动态添加或删除,并使得在类似情况发生时,系统可以自行生成新的策略来应对环境的变化。
(2)基于现有的条件反射模型的研究和条件反射学习算法,构建了一种新的基于条件反射的自适应策略管理模型。该模型是在IETF策略管理模型的基础上扩展而来,由几个简单的模块构成一个完整的反射弧。通过条件反射学习,系统可以动态监测到变化的环境条件,并生成相应的条件反射策略来应对。该模型能充分表现条件反射的自学习过程,从而可以将生物最基本的学习方式应用到策略管理中。
(3)由于当前缺乏统一标准,各厂商设备大都不支持COPS协议,故本文将COPS协议携带的策略信息转换为设备识别的SNMP管理信息,并充分利用SNMP和策略管理两种技术的优点、弥补其不足,提出了一种具有良好扩展性的网络管理模型PSBNM。该模型既克服了传统网管方式中扩展性差、不够灵活的缺点,又吸取了基于策略的网络管理中智能、自动化的优点,最终解决了由于设备更新换代困难而阻碍PBNM推广的问题。
(4)引入进化学习方法,实现了策略优化。策略管理应该具备一个类似“自然选择”的过程:有用的策略被保留,而淘汰那些过时的策略。针对策略的不同命中率设定策略的优先级,即:将高命中率的策略优先级提高,而低命中率的优先级降低,其中长期命中率为零的策略实施休眠。通过实现策略的“优胜劣汰”,从而进一步提高策略管理的自学习程度。
(5)根据校园网特点,将基于安全策略的网络管理进行应用研究,深入分析目前存在于校园网中的各类不安全因素:ARP欺骗、资源滥用、IP冒用、病毒传播,采用ARP欺骗检测、基于URL的内容过滤、网络接入安全认证等网络管理方法,运用策略管理的思想,提出针对性地解决方案,从而提高网络的安全程度。
总之,本文对基于智能策略的网络自管理模型及应用进行了较为深入地探索与钻研,在理论延伸与实际应用方面有所突破,得到了一些有益的经验和结论。
With the overwhelming development of network to large-scale,heterogeneity and high-speed,network management becomes more and more important.But the fast development of network results in low efficiency and high proneness to error.Network manager's enthusiasm shrivels when they confront such complex network.
To lighten the complexity of operation,and to improve the flexibility and autonomy of the management,the vision of autonomic computing(AC) is proposed. The essence of autonomic computing systems is self-management.Autonomic computing,as the name suggests,is a metaphor based on biology.The general properties of an autonomic(self-managing) system can be summarised as four objectives:self-configuring,self-healing,self-optimising and self-protecting.
With the future vision of AC,policy-based management(PBM) becomes a promising solution,which is developed to deliver simplification and automation of the network management process.The PBM,realizing by implement policies,is the embodiment of manager's global management ideas.PBM is one of the key but challenging areas of network management.But now PBM can not meet the need of AC, especially in self-management of policy,dynamic self-configuring,self-optimization, safely configuring.
In this dissertation,inspired by classical conditioning,the most basic learning mode of biology,we presented a method of intelligent policy description based on classical conditioning,and then we proposed a dynamic policy adaptation framework. To improve PBM to traditional network management,we put forword a model which combines traditional SNMP network management and PBM.In addition,in order to make the policy change with the environment,we propose a policy optimization method and policy dormant mechanism.At last,confronting insecurity factors of campus network,we bring forword corresponding security policy to deal with them.The main thesis research results and characteristics are as follows:
(1) Comparability between reflex,conditioned reflex and PBM is analyzed. Intelligent policy description based on classical conditioning is presented.The processes of reflex and conditioned reflex are described by policy,and some typical examples of classical conditioning are pictured.Then some typical network security cases are presented to testify how network management policies cater for the dynamic management of network security,and the selected network security policy is calculated and changed at run-time.So policy adapts dynamically by selecting and enabling/disabling a policy,or by learning the most suitable policy from the system behavior.
(2) An adaptive network policy management model is presented in this paper,and it is based on the theory of classical conditioning,which is a basic learning mode of biological system.The proposed framework is an extension of Internet Engineering Task Force(IETF) framework for policy-based network,and it is built with several simple building blocks as a complete reflex arc.In order to learning which are the most suitable configuration policies from the system behavior,the network rules specified within our framework are dynamically triggered by comparing training stimulus of the experiments.Our approach provides the flexibility to adapt to the changing of network environment and the ability to simulate some typical experiments of classical conditioning.Furthermore,the major advantage of this procedure is that the framework could successfully realize the self-learning process of classical conditioning and achieves an adaptive network policy management.
(3) The traditional management of centralized style cannot deal with the task of managing large-scaled distributed network.And policy-based management is one of the effective solutions in network and distributed systems management.By analyzing traditional network management and policy-based network management,a scalable network management model PSBNM is proposed,which takes full advantage of the two technologies and makes up the disadvantages of them.
(4) A policy optimization study based on evolution learning is proposed.A certain policy only suits to a certain network environment.If the network environment changes, the certain policy does not suit any more.Thereby,the policy-based management should also have similar "natural selection" process.Useful policy will be retained,and policies which have lost their effectiveness are eliminated.For different shooting times, the priority of policy with high shooting times is improved,while policy hit a low rate has lower priority,and long-term no shooting policy will be dormant.Thus the strategy for the survival of the fittest is realized,and the degree of self-learning in policy management is improved.
(5) Applications on policy-based network security management are carried on. Some security breaches in campus networks gradually emerged from the overwhelming development of network,specially represented by ARP snooping,or URL based content filtering,and network access security.They are deeply analyzed,and we propose solutions to these problems by using policy-based management and other network management technology.So information security of campus network is enhanced accordingly.
基于策略的网络管理作为自主计算研究的一个分支,它使得管理员能够从网络整体出发,以全局的观念进行策略配置,从而提高网络管理的可扩展性和灵活性,以实现网络管理的智能化。因此,策略管理在新一代网络管理研究中占据举足轻重的地位。然而,如何满足自主计算提出的要求,尤其是策略的自管理、动态自配置、自优化和安全可靠性等方面成为有待解决的关键性问题。
本论文正是针对以上问题,受生物智能中条件反射独特机理的启发,提出了基于条件反射的智能策略描述方法,并构建基于智能策略的网络自管理模型,以实现网络策略的自管理。为了更快地将策略管理推广到现有的网络管理中,本论文还研究了一个结合传统SNMP网络管理和策略管理的网管模型。此外,为了使策略能随环境自行改变调整,我们引入了策略优化方法以及策略休眠机制。最后,针对校园网中存在的各种不安全因素,运用相应的安全策略与管理模型,以增强校园网的可控性和安全性。本文的主要研究成果与特色如下:
(1)分析了反射及条件反射与策略管理的相似性,将反射及条件反射过程用策略进行描述,特别是将巴浦洛夫条件反射的各种经典实验用策略进行描述。随后相对应地给出一些与条件反射类似的网络安全案例,并对这些网络安全管理过程以条件反射学习策略的方式描述。从而将策略的动态自适应选择以条件反射的学习过程表示:当条件发生变化时,实现策略的动态添加或删除,并使得在类似情况发生时,系统可以自行生成新的策略来应对环境的变化。
(2)基于现有的条件反射模型的研究和条件反射学习算法,构建了一种新的基于条件反射的自适应策略管理模型。该模型是在IETF策略管理模型的基础上扩展而来,由几个简单的模块构成一个完整的反射弧。通过条件反射学习,系统可以动态监测到变化的环境条件,并生成相应的条件反射策略来应对。该模型能充分表现条件反射的自学习过程,从而可以将生物最基本的学习方式应用到策略管理中。
(3)由于当前缺乏统一标准,各厂商设备大都不支持COPS协议,故本文将COPS协议携带的策略信息转换为设备识别的SNMP管理信息,并充分利用SNMP和策略管理两种技术的优点、弥补其不足,提出了一种具有良好扩展性的网络管理模型PSBNM。该模型既克服了传统网管方式中扩展性差、不够灵活的缺点,又吸取了基于策略的网络管理中智能、自动化的优点,最终解决了由于设备更新换代困难而阻碍PBNM推广的问题。
(4)引入进化学习方法,实现了策略优化。策略管理应该具备一个类似“自然选择”的过程:有用的策略被保留,而淘汰那些过时的策略。针对策略的不同命中率设定策略的优先级,即:将高命中率的策略优先级提高,而低命中率的优先级降低,其中长期命中率为零的策略实施休眠。通过实现策略的“优胜劣汰”,从而进一步提高策略管理的自学习程度。
(5)根据校园网特点,将基于安全策略的网络管理进行应用研究,深入分析目前存在于校园网中的各类不安全因素:ARP欺骗、资源滥用、IP冒用、病毒传播,采用ARP欺骗检测、基于URL的内容过滤、网络接入安全认证等网络管理方法,运用策略管理的思想,提出针对性地解决方案,从而提高网络的安全程度。
总之,本文对基于智能策略的网络自管理模型及应用进行了较为深入地探索与钻研,在理论延伸与实际应用方面有所突破,得到了一些有益的经验和结论。
With the overwhelming development of network to large-scale,heterogeneity and high-speed,network management becomes more and more important.But the fast development of network results in low efficiency and high proneness to error.Network manager's enthusiasm shrivels when they confront such complex network.
To lighten the complexity of operation,and to improve the flexibility and autonomy of the management,the vision of autonomic computing(AC) is proposed. The essence of autonomic computing systems is self-management.Autonomic computing,as the name suggests,is a metaphor based on biology.The general properties of an autonomic(self-managing) system can be summarised as four objectives:self-configuring,self-healing,self-optimising and self-protecting.
With the future vision of AC,policy-based management(PBM) becomes a promising solution,which is developed to deliver simplification and automation of the network management process.The PBM,realizing by implement policies,is the embodiment of manager's global management ideas.PBM is one of the key but challenging areas of network management.But now PBM can not meet the need of AC, especially in self-management of policy,dynamic self-configuring,self-optimization, safely configuring.
In this dissertation,inspired by classical conditioning,the most basic learning mode of biology,we presented a method of intelligent policy description based on classical conditioning,and then we proposed a dynamic policy adaptation framework. To improve PBM to traditional network management,we put forword a model which combines traditional SNMP network management and PBM.In addition,in order to make the policy change with the environment,we propose a policy optimization method and policy dormant mechanism.At last,confronting insecurity factors of campus network,we bring forword corresponding security policy to deal with them.The main thesis research results and characteristics are as follows:
(1) Comparability between reflex,conditioned reflex and PBM is analyzed. Intelligent policy description based on classical conditioning is presented.The processes of reflex and conditioned reflex are described by policy,and some typical examples of classical conditioning are pictured.Then some typical network security cases are presented to testify how network management policies cater for the dynamic management of network security,and the selected network security policy is calculated and changed at run-time.So policy adapts dynamically by selecting and enabling/disabling a policy,or by learning the most suitable policy from the system behavior.
(2) An adaptive network policy management model is presented in this paper,and it is based on the theory of classical conditioning,which is a basic learning mode of biological system.The proposed framework is an extension of Internet Engineering Task Force(IETF) framework for policy-based network,and it is built with several simple building blocks as a complete reflex arc.In order to learning which are the most suitable configuration policies from the system behavior,the network rules specified within our framework are dynamically triggered by comparing training stimulus of the experiments.Our approach provides the flexibility to adapt to the changing of network environment and the ability to simulate some typical experiments of classical conditioning.Furthermore,the major advantage of this procedure is that the framework could successfully realize the self-learning process of classical conditioning and achieves an adaptive network policy management.
(3) The traditional management of centralized style cannot deal with the task of managing large-scaled distributed network.And policy-based management is one of the effective solutions in network and distributed systems management.By analyzing traditional network management and policy-based network management,a scalable network management model PSBNM is proposed,which takes full advantage of the two technologies and makes up the disadvantages of them.
(4) A policy optimization study based on evolution learning is proposed.A certain policy only suits to a certain network environment.If the network environment changes, the certain policy does not suit any more.Thereby,the policy-based management should also have similar "natural selection" process.Useful policy will be retained,and policies which have lost their effectiveness are eliminated.For different shooting times, the priority of policy with high shooting times is improved,while policy hit a low rate has lower priority,and long-term no shooting policy will be dormant.Thus the strategy for the survival of the fittest is realized,and the degree of self-learning in policy management is improved.
(5) Applications on policy-based network security management are carried on. Some security breaches in campus networks gradually emerged from the overwhelming development of network,specially represented by ARP snooping,or URL based content filtering,and network access security.They are deeply analyzed,and we propose solutions to these problems by using policy-based management and other network management technology.So information security of campus network is enhanced accordingly.
引文
[1]丁永生.计算智能:理论、技术与应用[M].科学出版社,2004.5.
[2]任立红,丁永生.一种新颖的基于生态网络计算的网络仿真平台[J].系统仿真学报,2002,14(11):1497-1499,1503.
[3]丁永生,任立红.一种基于免疫突现计算的生物网络结构的设计[J].控制与决策.2003,18(2):185—189.
[4]丁永生,任立红.人工免疫系统:理论与应用[J].模式识别与人工智能,2000,13(1):52-59.
[5]H.B.Sun,Y.S.Ding,K.R.Hao.Web service emergent system based on mechanism of bio-systemic intelligence[J].Dynamics of continuous discrete and impulsive systems-series b-applications & algorithms,2006,13(2):6-10.
[6]Y.S.Ding,H.B.Sun,K.R.Hao.A bio-inspired emergent system for intelligent Web service composition and management[J].Knowledge-Based Systems.2007,20(5):457-465.
[7]Y.S.Ding,L.Gao,D.Ruan.Communication mechanisms in ecological network -based grid middleware for service emergence[J].Information Sciences,2007,177(3):722-733.
[8]L.Gao,Y.S.Ding,H.Ying.An adaptive social network-inspired approach to resource discovery for the complex grid systems[J].International Journal of Intelligent Systems,2006,35(3):347-360.
[9]L.Gao,Y.S.Ding,L.H.Ren.A novel ecological network-based computation platform as grid middleware system[J].Int.J.Intelligent Systems,2004,19(10):859-884.
[10]M.Parashar,S.Hariri.Autonomic computing:an overview,International Workshop on Unconventional Programming Paradigms(UPP 2004)[C].Lecture Notes in Computer Science[J].2005,3566:257-269.
[11]廖备水,李石坚,姚远等.自主计算概念模型与实现方法[J].软件学报,2008,19(4):779-802.
[12]A.G.Ganek,T.A.Corbi.The dawning of the autonomic computing era[J].IBM Systems Jounal,2003,42(1):5-18.
[13]IBM.IBM Autonomic Computing[EB/OL].http://www.research.ibm.com/autonomic/.
[14]H.Tianfield,R.Unland.Towards autonomic computing systems[J].Engineering Application of Artificial Intelligence,2004,17(7):689-699.
[15]R.Sterritt.Autonomic computing[J].Innovations in System and Software Engineering,2005,1(1):79-88.
[16]J.O.Kephart,D.M.Chess.The vision of autonomic computing[J].IEEE Computer Society,2003,36(1):41-50.
[17]R.Sterritt,M.Parasher,H.Tianfield,et al.A concise introduction to autonomic computing[J].Advanced Engineering Informatics,2005,19(3):181-187.
[18]R.Barrett,P.P.Maglio,E.Kandogan,et al.Usable autonomic computing systems:the system administrators' perspective[J].Advanced Engineering Informatics,2005,19(3):213-221.
[19]刘朝阳.“电子蜥蜴”迈出自主计算第二步[J].互联网周刊,2001,32,82-85.
[20]罗杰文.主体技术为自治计算铺路[EB/OL].http://www.ict.ac.cn/news/channel/detail3776.asp.
[21]D.C.Verma,S.Sahu,S.Calo,et al.SRIRAM:A scalable resilient autonomic mesh[J].IBM Systems Jounal,2003,42(1):19-28.
[22]J.Jann,L.M.Browning,R.S.Burugula.Dynamic reconfiguration:Basic building blocks for autonomic computing on IBM pSeries servers[J].IBM Systems Jounal,2003,42(1):29-37.
[23]K.Whisnant,Z.T.Kalbarc,R.K.Iyer.A system model for dynamically reconfigurable software[J].IBM Systems Jounal,2003,42(1):45-59.
[24] D.M.Chess,C.C.Palmer,S.R.White.Security in an autonomic computing environment[J].IBM Systems Jounal,2003,42(1):107-118.
[25] S.R.White,M.Swimmer,E.J.Pring,et al.Anatomy of a commercial-grade immune system[C].Proceedings of the Ninth International Virus Bulletin Conference(VB 1999).
[26] S.Hofmeyrand,S.Forrest.Architecture for an artificial immune system[J].Evolutionary Computation,2000,7(1):1289-1296.
[27] A.Somayaji,S.Forrest.Automated response using system-call delays[C].Proceedings of the 9th conference on USENIX Security Symposium,Denver,Colorado,2000,9,1-14.
[28] Y.Diao,J.L.Hellerstein,S.Parekh,et al.Managing Web server performance with AutoTune agents[J].IBM Systems Jounal,2003,42(1):136-149.
[29] J.P.Bigus,D.A.Schlosnagle,J.R.Pilgrim,et al.ABLE:a toolkit for building multiagent autonomic systems[J].IBM Systems Jounal,2002,41(3):350-371
[30] J.P.Bigus,J.L.Hellerstein,M.S.Squillante.Auto Tune:a generic agent for automated performance tuning[C].Proceeding of the international conference on Practical application of intelligent agents and muti-agents(PAAM 2000),Manchester,UK.
[31] R.Haas,P.Droz,B.Stiller.Autonomic service deployment in networks[J].IBM Systems Jounal,2003,42(1):150-164.
[32] J.Appavoo,K.Hui,C.A.N.Soules,et al.Enabling autonomic behavior in systems software with hot swapping[J].IBM Systems Jounal,2003,42(1):60-76.
[33] D.M.Yellin.Competitive algorithms for the dynamic selection of component implementations[J].IBM Systems Jounal,2003,42(1):85-97.
[34] V.Markl,G M.Lohman,V.Raman.LEO:An autonomic query optimizer for DB2[J].IBM Systems Jounal,2003,42(1):98-106.
[35] G.Lanfranchi,P.D.Peruta,A.Perrone,et al.Toward a new landscape of system management in an autonomic computing environment[J].IBM Systems Jounal,2003,42(1):119-128.
[36]R.Want,T.Pering,D.Tennenhouse.Comparing autonomic and proactive computing[J].IBM Systems Jounal,2003,42(1):129-135.
[37]D.F.Bantz,C.Bisdikian,D.Challener,et al.Autonomic personal computing[J].IBM Systems Jounal,2003,42(1):165-176.
[38]D.M.Russell,P.P.Maglio,R.Dordick,et al.Dealing with Ghosts:Managing the user experience of autonomic computing[J].IBM Systems Jounal,2003,42(1):177-188.
[39]A.Abbondanzio,Y.Aridor,et al.Management of application complexes in multitier clustered systems[J].IBM Systems Jounal,2003,42(1):189-195.
[40]S.Uttamchandani,C.Talcott,D.Pease.Eos:an approach of using behavior implications for policy-based self-management[C].Proc.14th IFIP/IEEE Workshop Distributed Systems:Operations and Management(DSOM03),Heidelberg,Germany,2003,16-27.
[41]S.George,D.Evans,L.Davidson.A biologically inspired programming model for self-healing systems[C].Proc.1st ACM SIGSOFT Workshop Self-Healing Systems(WOSS 02),ACM Press,2002,102-104.
[42]M.Shaw.Self-healing:softening precision to avoid brittleness[C].Proc.1st ACM SIGSOFT Workshop Self-Healing Systems(WOSS 02),ACM Press,2002,111-113.
[43]K.Herrmann,G.M(u|¨)hl,K.Geihs.Self management:the solution to complexity or just another problem[J].IEEE Computer Society,2005,6(1):1-17.
[44]郭楠,赵宏.基于mobile agent的分布式网络自管理模型[J].通信学报,2003,24(3):130-138.
[45]李千目,赵学龙,张宏等.一种基于自愈策略的网络故障诊断系统[J].仪器仪表学报,2005,26(8):764-766.
[46]冯美玉,程胜,张勖等.Ad hoc网络中自愈路由协议研究[J].北京邮电大学 学报,2005,28(2):46-49.
[47]王凤先,田俊峰,刘鑫.分布式冗余服务的自适应配置管理策略[J].计算机工程与应用,2003,25(1):92-107.
[48]付长冬,舒继武,郑纬民等.基于自主运算的自管理的IP-SAN存储系统的体系结构与性能策略[J].高技术通讯,2004,14(3):1-5.
[49]付长冬,舒继武,郑纬民等.基于自主运算的自适应存储区域网络系统[J].软件学报,2004,15(7):1056-106.
[50]朱广蔚,谭建,杨献春等.在构件的多个实现间动态选择的算法[J].计算机应用研究,2004,21(9):19-24.
[51]卢联强,黄河,李伟琴.支持自愈恢复的MPLS路由算法[J].计算机工程与应用,2004,40(6):168-195.
[52]黄维雄,黄铭钧,陈建利等.一种基于自配置策略的新型Peer to Peer平台系统[J].软件学报,2003,14(2):237-246.
[53]薛涛,冯博琴.内容发布订阅系统路由算法和自配置策略研究[J].软件学报,2005,16(2):251-259.
[54]林冰玉,彭四伟,汪须忠.基于6 Sigma的流程自优化系统研究[J].计算机应用,2005,25(1):229-232.
[55]曹怀虎,余镇危,王银燕等.一种基于Overlay Network的自优化网格架构[J].计算机工程与应用,2005,41(23):129-152.
[56]魏常辉,夏克俭,曾德华.基于XML的自优化数据库连接池的设计与实现[J].计算机应用研究,2005,22(7):206-209.
[57]谢美意,冯玉才.自调优数据库研究综述[J].计算机科学,2005,32(9):82-85.
[58]朱树人,黄展林,李伟琴.分布式入侵检测系统中自保护代理的系统设计[J].计算机工程,2003,29(16):18-20.
[59]刘涛,曾国荪,吴长俊.异构网格环境下任务分配的自主计算方法[J].通信学报,2006,27(11):139-147.
[60]张海俊,史忠植.自主计算软件工程方法[J].小型微型计算机系统,2006,27(6):1077-1082.
[61] 王千祥,申峡嵘,梅宏．自适应软件初探[J]．计算机科学,2004,31(10):168-171.
[62] L.Lymberopoulos,E.Lupu,M.Sloman.An adaptive policy-based framework for network services management[J].Journal of Network and Systems Management,2003,11(3):277-303.
[63] L.Lymberopoulos,E.Lupu,M.Sloman,An adaptive policy based management framework for differentiated services networks[C].Proceedings of the 3rd IEEE Workshop on Policies for Distributed Systems and Networks(Policy 2002),Monterey,CA,2002,147-158.
[64] D.A.Norman,A.Ortony,D.M.Russell.Affect and machine design:lessons for the development of autonomous machines[J].IBM Systems Jounal,2003,42(1):38-44.
[65] L.W.Russell,S.P.Morgan,E.G Chron.Clockwork:A new movement in autonomic systems[J].IBM Systems Jounal,2003,42(1):77-84.
[66] H.Guo.A Bayesian approach for autonomic algorithm selection[C].IJCAI workshop on AI and autonomic computing:developing a research agenda for self-managing computer systems,Acapulco,Mexico,2003.
[67] T.Lau,D.Oblinger,L.Bergman,et al.Learning procedures for autonomic computing[C].IJCAI workshop on AI and autonomic computing:developing a research agenda for self-managing computer systems,Acapulco,Mexico,2003.
[68] IBM,IBM Autonomic Computing[EB/OL].http://www.alphaworks.ibm.com/ autonomic.
[69] A.G Ganek.Autonomic computing:implementing the vision[C].Keynote presentation at the autonomic computing workshop(AMS 2003),Seattle,2003.
[70] G.Kaiser,J.Parekh,P.Gross,et al.Kinesthetics extreme:an external infrastructure for monitoring distributed legacy systems[C].Proceedings of the autonomic computing workshop,5th international workshop on active middleware services(AMS 2003),Seattle,2003,22-30.
[71]K.P.Birman,R.R.Van,W.Vogels.Navigating in the storm:using astrolabe for distributed self-configuration,monitoring and adaptation[C].Proceedings of the autonomic computing workshop,5th international workshop on active middleware services(AMS 2003),Seattle,2003,4-13.
[72]M.Agarwal,V.Bhat,H.Liu,et al.Automate:enabling autonomic applications on the grid[C].Proceedings of the autonomic computing workshop,5th international workshop on active middleware services(AMS 2003),Seattle,2003,22-30.
[73]H.Tianfield.Multi-agent based autonomic architecture for network management[C].Proceedings of the IEEE international conference,2003,21-24.
[74]HP,HP Adaptive Infrastructure:Building an Adaptive Enterprise[EB/OL].http://www.hp.com/hpinfo/newsroom/press_kits/2006/blades/bg_ai.pdf
[75]Sun Microsystems,N1-Introducing just-in-time computing[EB/OL].http://www.sun.com/software/n1gridsystem/wp-n1.pdf
[76]Microsoft Corporation,Dynamic systems initiative overview[EB/OL].http://www.microsoft.com/business/dsi/dsiwp.mspx.
[77]IBM and Cisco Systems,Adaptive services framework[EB/OL].White paper,version 1.0,2003,October 14.
[78]R.Want,T.Pering,D.Tennenhouse,Comparing autonomic and proactive computing[J].IBM Systems Jounal,2003,42(1):129-135.
[79]卢世凤,刘学敏,刘淘英等.基于策略的管理综述[J].计算机工程与应用,2004,40(9):85-89.
[80]马育峰,胡修林,张蕴玉.网络管理热点问题研究的现状、问题与展望[J].计算机应用研究,2005,22(3):10-13.
[81]R.Braden,D.Clark,S.Shenker,Integrated services in the Internet architecture:an overview[S].IETF RFC 1633,June 1994.
[82]S.Blake,D.Black,M.Carlson,et al.An architecture for differentiated service[S].IETF RFC 2475,December 1998.
[83] A.Westerinen,J.Schnizlein,J.Strassner,et al.Terminology for policy based management[S].IETF RFC 3198,November 2001.
[84] R.Yavatkar,D.Pendarakis,R.Guerin.A framework for policy based admission control[S].IETF RFC 2753,January 2000.
[85] J.Case,M.Fedor,M.Schoffstall,et al.Simple network management protocol(SNMP)[S].IETF RFC 1157,May 1990.
[86] V.Sander,W.A.Adamson,I.Foster,et al,End-to-end provision of policy information for network QoS[C].Proceedings of the Tenth IEEE Symposium on High Performance Distributed Computing(HPDC-10),IEEE Press,August 2001.
[87] M.Wahl,T.Hower,S.Kille.Lightweight directory access protocol(v3)[S].IETF RFC 2251,December 1997.
[88] K..L.E.Law,A.Saxena.Scalable design of a policy-based management system and its performance[J].IEEE Communication Magazine,2003,6804(3):72-79.
[89] K.L.E.Law,A.Saxena.UPM:unified policy-based network management[C].Proc.SPIEITCom,Denver,USA,2001,4523(8):326-337.
[90] A.Saxena.Unified policy-based management[D].National Library of Canada,2002.
[91] J.Vivero,J.Serrat.MANBOP:Management of active networks based on policies[J].IEEE Communications Magazine,2002,(7):135-139.
[92] B.Moore,E.Ellesson,J.Strassner,et al.Policy core information model-version 1 specification[S].RFC3060,February 2001.
[93] J.Boyle,R.Cohen,S.Herzog,et al.The COPS(Common Open Policy Service)protocol[S].IETF RFC 2748,January 2000.
[94] K.Chan,J.Seligson,D.Durham,et al.COPS usage for policy provisioning(COPS-PR)[S].RFC3084,March 2001.
[95] J.Boyle,R.Cohen,D.Durham,et al.COPS usage for RSVP[S].RFC2749,January 2000.
[96]Cisco Systems.思科QoS策略管理器解决方案[EB/OL].http://www.cisco.com/global/CN/products/wr2k/cw2000qos_1.shtml,2003.
[97]福建星网锐捷网络有限公司.全局安全网络解决方案[EB/OL].http://www.ruijie.com.cn/project/channel/Detail.asp?Content_id=1304,2005.
[98]邹一鸣,王汝传.一种基于策略和移动代理的网络管理体系结构[J].南京邮电学院学报,2005,25(1):90-94.
[99]李青虹,郭洪,施晓煌.一种基于策略和Web的分布式网络管理模型的研究[J].福州大学学报,2005,33(1):20-23.
[100]张彬,孙钢,张闯等.一种基于策略的新型主动网络管理系统[J].计算机应用研究,2004,21(3):174-177.
[101]黄海音,郭学理,孙立涛.一种基于策略的主动网络管理模型[J].计算机应用,2004,24(3):16-17.
[102]张彬,郭军.主动网络下基于策略的网络管理系统的研究与实现[J].计算机应用与软件,2004,21(4):6-7.
[103]J.S.Susan.Policy-based networks:hype and hope[J].IT Professional,2000,2(1):12-16.
[104]C.K.Wang.Policy-based network management[C].International Conference on Communication Technology Proceedings(WCCICCT'00),2000,101-105.
[105]N.S.Gary,L.Bert,G.X.Geoffrey.Network policy languages:a survey and a new approach[J].IEEE Network,2001,15(1):10-21.
[106]徐晓宇,张惠民.一种新的基于策略的端到端移动QoS体系架构[J].北京邮电大学学报,2004,27(6):54-58.
[107]张鹏亮,杨建刚.基于多Agent的策略驱动网络管理体系结构[J].计算机应用,2005,25(8):1753-1759.
[108]F.J.G.Clemente,G.M.P(?)rez,A.F.G.Skarmeta.An XML-seamless policy based management framework[C].Third International Workshop on Mathematical Methods,Models,and Architectures for Computer Network Security,MMM-ACNS 2005,Russia,September 2005.
[109]N.Samaan,A.Karmouch.An automated policy-based management framework for differentiated communication systems[J].IEEE Jounal on Selected Areas in Communications,2005,23(12):2236-2247.
[110]张少俊,李建华,郑明磊.基于策略的网络管理[J].计算机工程,2003,29(16):127-129.
[111]李金平,高东杰.基于策略的网络管理系统软件平台的研究及实现[J].计算机工程与应用,2002,38(12):177-179.
[112]E.C.Lupu,M.Sloman.Conflicts in policy-based distributed systems management[J].IEEE Transactions on Software Engineering,1999,25(6):852-869.
[113]A.Ponnappan,L.J.Yang,R.R.Pillai,et al.A policy based QoS management system for the IntServ/DiffServ based Interaet[C].Proceeding of the Third International Workshop on Policies for Distributed Systems and Networks (POLICY'02),2002,159-168.
[114]J.Hayes.Policy-based authentication and authorization:secure access to the network infrastructure[C].16th Annual Conference of the Computer Security Applications(ACSAC'00),2000,328-333.
[115]韩菊松,许华虎,余俊.基于策略的网络QoS管理[J].计算机工程,2004,30(8):114-116.
[116]袁满,胡建平,何锐等.基于策略的网格应用服务QoS管理模型[J].北京航空航天大学,2005,31(6):700-704.
[117]赵海萍,赵东风.基于Web2K服务器的QoS调度策略分析[J].计算机工程与应用,2005,41(13):125-128.
[118]李冬,汪东升,王耀才.一种QoS保证的存储虚拟化策略[J].计算机工程与应用,2005,41(11):15-25.
[119]H.J.Lim,K.J.Chun,T.M.Chung.Adaptive bandwidth control using fuzzy inference in policy-based network management[C].Information Networking:Convergence in Broadband and Mobile Networking(ICOIN 2005),LNCS 3391, 2005,443-451.
[120]张新跃,邓炜春,沈树群.策略驱动的网络安全管理模型[J].计算机应用研究,2005,22(1):226-231.
[121]张文凯,曹元大.应用安全平台体系中安全策略模型的研究[J].计算机工程,2005,31(1):136-162.
[122]姚键,孙昌平,孙虎等.基于策略的安全管理研究[J].计算机应用与软件,2005,22(3):115-119.
[123]李小平,吴琼,董庆宽等.IPv6网络中多级多域安全策略系统研究[J].西安电子科技大学学报,2008,35(2):300-304.
[124]陈卓.基于形式化与图形化的IPSec安全策略管理[J].计算机工程,2007,33(5):123-125.
[125]董守玲,谭彦,张凌.一个通用可扩展的网络安全策略管理系统[J].2005,45(增刊):172-175.
[126]李汉菊,张辉艳,李之棠.基于CORBA的分布式VPN策略管理的研究[J].2005,33(6):1-3.
[127]张文凯,曹元大.应用安全平台体系中安全策略模型的研究[J].2005,31(1):136-138.
[128]侯洪志.基于策略的QoS网络管理系统中PDP的研究与设计[D].长春:吉林大学,2004.
[129]金铭.基于策略的QoS网络管理系统中COPS协议研究与实现[D].长春:吉林大学,2004.
[130]B.Moore.Policy core information model extensions[S].RFC 3460,January 2003.
[131]王玢,左明雪.人体及动物生理学[M].高等教育出版社,2001.
[132]杨贝贝,阮晓刚.基于尖峰神经元的条件反射模型及其认知行为的研究[J].系统仿真学报,2005,17(9):2134-2137.
[133]R.A.Rescorla,A.R.Wagner.A theory of Pavlovian conditioning:variations in the efectiveness of reinforcement and nonreinforecement[C].Classical Conditioning II:current research and theory,New York,1972,64-99.
[134]A.R.Wagner.SOP:a model of automatic memory processing in animal behavior[C].Information Processing in Animals:Memory Mechanisms,Erlbaum,New Jersey,1981,85,5-44.
[135]R.S.Sutton,A.G Barto.Toward a modern theory of adaptive networks:expectation and prediction[J].Psychological Review,1981,88(2):135-170.
[136]R.S.Suton,A.G Barto.Time-derivative models of pavlovian reinforcement[M].Learning and computational neuronscience:foundations of adaptive networks,Cambridge,MA:MIT Press,1990.
[137]R.S.Suton,A.G Barto.Reinforcement learning:an introduction[M].Cambridge,MA:MIT Press,1998.
[138]A.H.Klopf.The hedonistic neuron:a theory of memory,learning and intelligence[M].Washinton D C,Hemisphere,1982.
[139]A.H.Klopf.A neuronal model of classical conditioning[J].Psychobiology,1988,16(2):85-125.
[140]G Stephen,A.S.Nestor.Neural dynamics of adaptive timing and temporal discrimination during associative learning[J].Neural Networks,1989,2(2):79-102.
[141]N.A.Schmajuk,J.J.DiCarlo.Stimulus configuration,classical conditioning,and hippocampal function[J].Psychological Review,1992,99(1):268-305.
[142]N.A.Schmajuk.Animal Learning and Cognition:A Neural Network Approach[M].Cambridge University Press,1997.
[143]C.Balkenius.Natural intelligence in artificial creatures[D].Lund University Cognitive Studies 37,1995.
[144]C.Balkenius.A neural network model of classical conditioning I:The dynamics of learning[D].Lund University Cognitive Studies 68,1998.
[145]C.Balkenius,J.Moren.Dynamics of a Classical Conditioning Model[J].Autonomous Robots,1999,7(1):41-56.
[146]R.Malaka,R.Lange,M.Hammer.A comparative study of real-time models for classical conditioning[C].Learning and Memory:Proceedings of the 23rd Gottingen Neurobiology Conference,Stuttgart,New York,1995,74-80.
[147]R.Malaka,R.Lange,M.Hammer.A constant prediction model for classical conditioning[C].Learning and Memory:Proceedings of the 23rd Gotten Neurobiology Conference,Stuttgart,New York,1995,75-80.
[148]A.Mignault,A.A.J.Marley,A real-time neuronal model of classical conditioning [J].Adaptive Behavior,1997,6(1):3-61.
[149]J.Christopher,L.Anders.An associative neural network model of classical conditioning[R].TRITA-NA-P0217,2002.
[150]T.Yamazaki,S.Tanaka.A neural network model for trace conditioning[J].International Journal of Neural Systems,2005,15(1 & 2):23-30.
[151]杨贝贝,阮晓刚.经典条件反射认知模型及其在运动控制系统中的应用[D].北京:北京工业大学,2005年.
[152]顾冬雷,陈卫东,席裕庚.移动机器人条件反射能力的实现[J].机器人,2001,23(2):123—126.
[153]R.I.Damper,R.L.B.French,T.W.Scutt.ARBIB:an Autonomous Robot Based on Inspirations from Biology[J].Robotics and Autonomous Systems,2000,31(4):247-274.
[154]胡新宇,赵明富,余成波等.机械定位控制的条件反射感知控制方法研究[J].中国制造业信息化,2004,33(10):111-113.
[155]L.C.Daniel,A comparison of some neural network models of classical conditioning[C].Intelligent Control,Proceedings 5th IEEE International Symposium,Philadelphia,PA,1990,1163-1168.
[156]R.Malaka,M.Hammer.Real-time models of classical conditioning[C].Neural Networks,Proceedings of IEEE International Conference,Washington DC,USA,1996,768-773.
[157]R.Malaka.Models of classical conditioning[J].Bulletin of Mathematical Biology,1999,61(1):33-83.
[158]C.R.Gallistel,J.Gibbon.Computational Versus Associative Models of Simple Conditioning[J].Current Directions in Psychological Science,2001,10:146-150.
[159]赵季红.下一代网络的网络管理技术[J].西安邮电学院学报,2004,9(1):1-5.
[160]S.Hahn,K.Chan,K.MCloghrie.Framework Policy Information Base[S].RFC 3318,March 2003.
[161]K.Chan,R.Sahita,S.Hahn,et al.Differentiated services quality of service policy information base[S].RFC 3317,March 2003.
[162]D.Rawlins,A.Kulkarni,et al.Framework policy information base for usage feedback[S].RFC3571,August 2003.
[163]R.M.Habib,M.S.Hasan,L.Caro.A new genetic algorithm with Lamarckian individual learning for generation scheduling[J].IEEE Transactions on Power Systems,2003,18(3):1181-1186.
[164]阎岭,蒋静坪.进化学习策略收敛性和逃逸能力的研究[J].自动化学报,2005,31(6):873-880.
[165]王洪峰,汪定伟,杨圣祥.动态环境中的进化算法[J].控制与决策,2007,22(2):127-131,137.
[166]程博,郭振宇,王军平等.一种并行免疫进化策略算法研究[J].控制与决策,2007,22(12):1395-1398,1402.
[167]任峡,吕述望.ARP协议欺骗原理分析与抵御方法[J].计算机工程,2003,29(9):127-182.
[168]徐丹,黎俊伟,高传善.基于ETHERNET的网络监听以及ARP欺骗[J].计算机应用与软件,2005,22(11):105-107.
[169]李海鹰,程灏,吕志强等.针对ARP攻击的网络防御模式设计与实现[J].计算机工程,2005,31(5):170-171.
[170]李艇.网络管理技术与应用[M].北京:高等教育出版社,2003.
[171]AdventNet Inc.Java SNMP API Documentation[EB/OL].http://snmp.adventnet. com,2005.
[172]刘琦,李建华.网络内容安全监管系统的框架及其关键技术[J].计算机工程,2003,29(2):287-289.
[173]杨经,司志刚,叶惠敏.基于内容安全的局域网监控系统的设计与实现[J].计算机工程与应用,2002,38(19):174-176,200.
[174]赵仲孟,陈剑,张选平等.网络内容安全分析与对策[J].计算机工程,2002,28(5):41—43.
[175]周澔宇.基于URL的网页内容过滤器的设计与实现[J].计算机工程,2006,32(7):81-83.
[176]M.Hammami,Y.Chahir,L.M.Chen.WebGuard:web based adult content detection and filtering System[C].Proceedings of the IEEE/WIC International conference on web Intelligence(WI'03),Halifax,Canada,2003,574-578.
[177]彭伟.使用802.1x实现校园网认证[J].计算机应用,2003,23(3):85-87.
[178]朱海龙,张国清.基于802.1x的以太网接入技术[J].计算机工程,2003,29(18):130-132.
[179]高祥,周林.802.1x协议及其在宽带接入中的应用[J].重庆邮电学院学报,2004,16(1):91-97.
[180]蒋海峰,苗放.802.1x标准和Radius协议的扩展应用研究[J].计算机应用,2003,23(11):88-90.
[2]任立红,丁永生.一种新颖的基于生态网络计算的网络仿真平台[J].系统仿真学报,2002,14(11):1497-1499,1503.
[3]丁永生,任立红.一种基于免疫突现计算的生物网络结构的设计[J].控制与决策.2003,18(2):185—189.
[4]丁永生,任立红.人工免疫系统:理论与应用[J].模式识别与人工智能,2000,13(1):52-59.
[5]H.B.Sun,Y.S.Ding,K.R.Hao.Web service emergent system based on mechanism of bio-systemic intelligence[J].Dynamics of continuous discrete and impulsive systems-series b-applications & algorithms,2006,13(2):6-10.
[6]Y.S.Ding,H.B.Sun,K.R.Hao.A bio-inspired emergent system for intelligent Web service composition and management[J].Knowledge-Based Systems.2007,20(5):457-465.
[7]Y.S.Ding,L.Gao,D.Ruan.Communication mechanisms in ecological network -based grid middleware for service emergence[J].Information Sciences,2007,177(3):722-733.
[8]L.Gao,Y.S.Ding,H.Ying.An adaptive social network-inspired approach to resource discovery for the complex grid systems[J].International Journal of Intelligent Systems,2006,35(3):347-360.
[9]L.Gao,Y.S.Ding,L.H.Ren.A novel ecological network-based computation platform as grid middleware system[J].Int.J.Intelligent Systems,2004,19(10):859-884.
[10]M.Parashar,S.Hariri.Autonomic computing:an overview,International Workshop on Unconventional Programming Paradigms(UPP 2004)[C].Lecture Notes in Computer Science[J].2005,3566:257-269.
[11]廖备水,李石坚,姚远等.自主计算概念模型与实现方法[J].软件学报,2008,19(4):779-802.
[12]A.G.Ganek,T.A.Corbi.The dawning of the autonomic computing era[J].IBM Systems Jounal,2003,42(1):5-18.
[13]IBM.IBM Autonomic Computing[EB/OL].http://www.research.ibm.com/autonomic/.
[14]H.Tianfield,R.Unland.Towards autonomic computing systems[J].Engineering Application of Artificial Intelligence,2004,17(7):689-699.
[15]R.Sterritt.Autonomic computing[J].Innovations in System and Software Engineering,2005,1(1):79-88.
[16]J.O.Kephart,D.M.Chess.The vision of autonomic computing[J].IEEE Computer Society,2003,36(1):41-50.
[17]R.Sterritt,M.Parasher,H.Tianfield,et al.A concise introduction to autonomic computing[J].Advanced Engineering Informatics,2005,19(3):181-187.
[18]R.Barrett,P.P.Maglio,E.Kandogan,et al.Usable autonomic computing systems:the system administrators' perspective[J].Advanced Engineering Informatics,2005,19(3):213-221.
[19]刘朝阳.“电子蜥蜴”迈出自主计算第二步[J].互联网周刊,2001,32,82-85.
[20]罗杰文.主体技术为自治计算铺路[EB/OL].http://www.ict.ac.cn/news/channel/detail3776.asp.
[21]D.C.Verma,S.Sahu,S.Calo,et al.SRIRAM:A scalable resilient autonomic mesh[J].IBM Systems Jounal,2003,42(1):19-28.
[22]J.Jann,L.M.Browning,R.S.Burugula.Dynamic reconfiguration:Basic building blocks for autonomic computing on IBM pSeries servers[J].IBM Systems Jounal,2003,42(1):29-37.
[23]K.Whisnant,Z.T.Kalbarc,R.K.Iyer.A system model for dynamically reconfigurable software[J].IBM Systems Jounal,2003,42(1):45-59.
[24] D.M.Chess,C.C.Palmer,S.R.White.Security in an autonomic computing environment[J].IBM Systems Jounal,2003,42(1):107-118.
[25] S.R.White,M.Swimmer,E.J.Pring,et al.Anatomy of a commercial-grade immune system[C].Proceedings of the Ninth International Virus Bulletin Conference(VB 1999).
[26] S.Hofmeyrand,S.Forrest.Architecture for an artificial immune system[J].Evolutionary Computation,2000,7(1):1289-1296.
[27] A.Somayaji,S.Forrest.Automated response using system-call delays[C].Proceedings of the 9th conference on USENIX Security Symposium,Denver,Colorado,2000,9,1-14.
[28] Y.Diao,J.L.Hellerstein,S.Parekh,et al.Managing Web server performance with AutoTune agents[J].IBM Systems Jounal,2003,42(1):136-149.
[29] J.P.Bigus,D.A.Schlosnagle,J.R.Pilgrim,et al.ABLE:a toolkit for building multiagent autonomic systems[J].IBM Systems Jounal,2002,41(3):350-371
[30] J.P.Bigus,J.L.Hellerstein,M.S.Squillante.Auto Tune:a generic agent for automated performance tuning[C].Proceeding of the international conference on Practical application of intelligent agents and muti-agents(PAAM 2000),Manchester,UK.
[31] R.Haas,P.Droz,B.Stiller.Autonomic service deployment in networks[J].IBM Systems Jounal,2003,42(1):150-164.
[32] J.Appavoo,K.Hui,C.A.N.Soules,et al.Enabling autonomic behavior in systems software with hot swapping[J].IBM Systems Jounal,2003,42(1):60-76.
[33] D.M.Yellin.Competitive algorithms for the dynamic selection of component implementations[J].IBM Systems Jounal,2003,42(1):85-97.
[34] V.Markl,G M.Lohman,V.Raman.LEO:An autonomic query optimizer for DB2[J].IBM Systems Jounal,2003,42(1):98-106.
[35] G.Lanfranchi,P.D.Peruta,A.Perrone,et al.Toward a new landscape of system management in an autonomic computing environment[J].IBM Systems Jounal,2003,42(1):119-128.
[36]R.Want,T.Pering,D.Tennenhouse.Comparing autonomic and proactive computing[J].IBM Systems Jounal,2003,42(1):129-135.
[37]D.F.Bantz,C.Bisdikian,D.Challener,et al.Autonomic personal computing[J].IBM Systems Jounal,2003,42(1):165-176.
[38]D.M.Russell,P.P.Maglio,R.Dordick,et al.Dealing with Ghosts:Managing the user experience of autonomic computing[J].IBM Systems Jounal,2003,42(1):177-188.
[39]A.Abbondanzio,Y.Aridor,et al.Management of application complexes in multitier clustered systems[J].IBM Systems Jounal,2003,42(1):189-195.
[40]S.Uttamchandani,C.Talcott,D.Pease.Eos:an approach of using behavior implications for policy-based self-management[C].Proc.14th IFIP/IEEE Workshop Distributed Systems:Operations and Management(DSOM03),Heidelberg,Germany,2003,16-27.
[41]S.George,D.Evans,L.Davidson.A biologically inspired programming model for self-healing systems[C].Proc.1st ACM SIGSOFT Workshop Self-Healing Systems(WOSS 02),ACM Press,2002,102-104.
[42]M.Shaw.Self-healing:softening precision to avoid brittleness[C].Proc.1st ACM SIGSOFT Workshop Self-Healing Systems(WOSS 02),ACM Press,2002,111-113.
[43]K.Herrmann,G.M(u|¨)hl,K.Geihs.Self management:the solution to complexity or just another problem[J].IEEE Computer Society,2005,6(1):1-17.
[44]郭楠,赵宏.基于mobile agent的分布式网络自管理模型[J].通信学报,2003,24(3):130-138.
[45]李千目,赵学龙,张宏等.一种基于自愈策略的网络故障诊断系统[J].仪器仪表学报,2005,26(8):764-766.
[46]冯美玉,程胜,张勖等.Ad hoc网络中自愈路由协议研究[J].北京邮电大学 学报,2005,28(2):46-49.
[47]王凤先,田俊峰,刘鑫.分布式冗余服务的自适应配置管理策略[J].计算机工程与应用,2003,25(1):92-107.
[48]付长冬,舒继武,郑纬民等.基于自主运算的自管理的IP-SAN存储系统的体系结构与性能策略[J].高技术通讯,2004,14(3):1-5.
[49]付长冬,舒继武,郑纬民等.基于自主运算的自适应存储区域网络系统[J].软件学报,2004,15(7):1056-106.
[50]朱广蔚,谭建,杨献春等.在构件的多个实现间动态选择的算法[J].计算机应用研究,2004,21(9):19-24.
[51]卢联强,黄河,李伟琴.支持自愈恢复的MPLS路由算法[J].计算机工程与应用,2004,40(6):168-195.
[52]黄维雄,黄铭钧,陈建利等.一种基于自配置策略的新型Peer to Peer平台系统[J].软件学报,2003,14(2):237-246.
[53]薛涛,冯博琴.内容发布订阅系统路由算法和自配置策略研究[J].软件学报,2005,16(2):251-259.
[54]林冰玉,彭四伟,汪须忠.基于6 Sigma的流程自优化系统研究[J].计算机应用,2005,25(1):229-232.
[55]曹怀虎,余镇危,王银燕等.一种基于Overlay Network的自优化网格架构[J].计算机工程与应用,2005,41(23):129-152.
[56]魏常辉,夏克俭,曾德华.基于XML的自优化数据库连接池的设计与实现[J].计算机应用研究,2005,22(7):206-209.
[57]谢美意,冯玉才.自调优数据库研究综述[J].计算机科学,2005,32(9):82-85.
[58]朱树人,黄展林,李伟琴.分布式入侵检测系统中自保护代理的系统设计[J].计算机工程,2003,29(16):18-20.
[59]刘涛,曾国荪,吴长俊.异构网格环境下任务分配的自主计算方法[J].通信学报,2006,27(11):139-147.
[60]张海俊,史忠植.自主计算软件工程方法[J].小型微型计算机系统,2006,27(6):1077-1082.
[61] 王千祥,申峡嵘,梅宏．自适应软件初探[J]．计算机科学,2004,31(10):168-171.
[62] L.Lymberopoulos,E.Lupu,M.Sloman.An adaptive policy-based framework for network services management[J].Journal of Network and Systems Management,2003,11(3):277-303.
[63] L.Lymberopoulos,E.Lupu,M.Sloman,An adaptive policy based management framework for differentiated services networks[C].Proceedings of the 3rd IEEE Workshop on Policies for Distributed Systems and Networks(Policy 2002),Monterey,CA,2002,147-158.
[64] D.A.Norman,A.Ortony,D.M.Russell.Affect and machine design:lessons for the development of autonomous machines[J].IBM Systems Jounal,2003,42(1):38-44.
[65] L.W.Russell,S.P.Morgan,E.G Chron.Clockwork:A new movement in autonomic systems[J].IBM Systems Jounal,2003,42(1):77-84.
[66] H.Guo.A Bayesian approach for autonomic algorithm selection[C].IJCAI workshop on AI and autonomic computing:developing a research agenda for self-managing computer systems,Acapulco,Mexico,2003.
[67] T.Lau,D.Oblinger,L.Bergman,et al.Learning procedures for autonomic computing[C].IJCAI workshop on AI and autonomic computing:developing a research agenda for self-managing computer systems,Acapulco,Mexico,2003.
[68] IBM,IBM Autonomic Computing[EB/OL].http://www.alphaworks.ibm.com/ autonomic.
[69] A.G Ganek.Autonomic computing:implementing the vision[C].Keynote presentation at the autonomic computing workshop(AMS 2003),Seattle,2003.
[70] G.Kaiser,J.Parekh,P.Gross,et al.Kinesthetics extreme:an external infrastructure for monitoring distributed legacy systems[C].Proceedings of the autonomic computing workshop,5th international workshop on active middleware services(AMS 2003),Seattle,2003,22-30.
[71]K.P.Birman,R.R.Van,W.Vogels.Navigating in the storm:using astrolabe for distributed self-configuration,monitoring and adaptation[C].Proceedings of the autonomic computing workshop,5th international workshop on active middleware services(AMS 2003),Seattle,2003,4-13.
[72]M.Agarwal,V.Bhat,H.Liu,et al.Automate:enabling autonomic applications on the grid[C].Proceedings of the autonomic computing workshop,5th international workshop on active middleware services(AMS 2003),Seattle,2003,22-30.
[73]H.Tianfield.Multi-agent based autonomic architecture for network management[C].Proceedings of the IEEE international conference,2003,21-24.
[74]HP,HP Adaptive Infrastructure:Building an Adaptive Enterprise[EB/OL].http://www.hp.com/hpinfo/newsroom/press_kits/2006/blades/bg_ai.pdf
[75]Sun Microsystems,N1-Introducing just-in-time computing[EB/OL].http://www.sun.com/software/n1gridsystem/wp-n1.pdf
[76]Microsoft Corporation,Dynamic systems initiative overview[EB/OL].http://www.microsoft.com/business/dsi/dsiwp.mspx.
[77]IBM and Cisco Systems,Adaptive services framework[EB/OL].White paper,version 1.0,2003,October 14.
[78]R.Want,T.Pering,D.Tennenhouse,Comparing autonomic and proactive computing[J].IBM Systems Jounal,2003,42(1):129-135.
[79]卢世凤,刘学敏,刘淘英等.基于策略的管理综述[J].计算机工程与应用,2004,40(9):85-89.
[80]马育峰,胡修林,张蕴玉.网络管理热点问题研究的现状、问题与展望[J].计算机应用研究,2005,22(3):10-13.
[81]R.Braden,D.Clark,S.Shenker,Integrated services in the Internet architecture:an overview[S].IETF RFC 1633,June 1994.
[82]S.Blake,D.Black,M.Carlson,et al.An architecture for differentiated service[S].IETF RFC 2475,December 1998.
[83] A.Westerinen,J.Schnizlein,J.Strassner,et al.Terminology for policy based management[S].IETF RFC 3198,November 2001.
[84] R.Yavatkar,D.Pendarakis,R.Guerin.A framework for policy based admission control[S].IETF RFC 2753,January 2000.
[85] J.Case,M.Fedor,M.Schoffstall,et al.Simple network management protocol(SNMP)[S].IETF RFC 1157,May 1990.
[86] V.Sander,W.A.Adamson,I.Foster,et al,End-to-end provision of policy information for network QoS[C].Proceedings of the Tenth IEEE Symposium on High Performance Distributed Computing(HPDC-10),IEEE Press,August 2001.
[87] M.Wahl,T.Hower,S.Kille.Lightweight directory access protocol(v3)[S].IETF RFC 2251,December 1997.
[88] K..L.E.Law,A.Saxena.Scalable design of a policy-based management system and its performance[J].IEEE Communication Magazine,2003,6804(3):72-79.
[89] K.L.E.Law,A.Saxena.UPM:unified policy-based network management[C].Proc.SPIEITCom,Denver,USA,2001,4523(8):326-337.
[90] A.Saxena.Unified policy-based management[D].National Library of Canada,2002.
[91] J.Vivero,J.Serrat.MANBOP:Management of active networks based on policies[J].IEEE Communications Magazine,2002,(7):135-139.
[92] B.Moore,E.Ellesson,J.Strassner,et al.Policy core information model-version 1 specification[S].RFC3060,February 2001.
[93] J.Boyle,R.Cohen,S.Herzog,et al.The COPS(Common Open Policy Service)protocol[S].IETF RFC 2748,January 2000.
[94] K.Chan,J.Seligson,D.Durham,et al.COPS usage for policy provisioning(COPS-PR)[S].RFC3084,March 2001.
[95] J.Boyle,R.Cohen,D.Durham,et al.COPS usage for RSVP[S].RFC2749,January 2000.
[96]Cisco Systems.思科QoS策略管理器解决方案[EB/OL].http://www.cisco.com/global/CN/products/wr2k/cw2000qos_1.shtml,2003.
[97]福建星网锐捷网络有限公司.全局安全网络解决方案[EB/OL].http://www.ruijie.com.cn/project/channel/Detail.asp?Content_id=1304,2005.
[98]邹一鸣,王汝传.一种基于策略和移动代理的网络管理体系结构[J].南京邮电学院学报,2005,25(1):90-94.
[99]李青虹,郭洪,施晓煌.一种基于策略和Web的分布式网络管理模型的研究[J].福州大学学报,2005,33(1):20-23.
[100]张彬,孙钢,张闯等.一种基于策略的新型主动网络管理系统[J].计算机应用研究,2004,21(3):174-177.
[101]黄海音,郭学理,孙立涛.一种基于策略的主动网络管理模型[J].计算机应用,2004,24(3):16-17.
[102]张彬,郭军.主动网络下基于策略的网络管理系统的研究与实现[J].计算机应用与软件,2004,21(4):6-7.
[103]J.S.Susan.Policy-based networks:hype and hope[J].IT Professional,2000,2(1):12-16.
[104]C.K.Wang.Policy-based network management[C].International Conference on Communication Technology Proceedings(WCCICCT'00),2000,101-105.
[105]N.S.Gary,L.Bert,G.X.Geoffrey.Network policy languages:a survey and a new approach[J].IEEE Network,2001,15(1):10-21.
[106]徐晓宇,张惠民.一种新的基于策略的端到端移动QoS体系架构[J].北京邮电大学学报,2004,27(6):54-58.
[107]张鹏亮,杨建刚.基于多Agent的策略驱动网络管理体系结构[J].计算机应用,2005,25(8):1753-1759.
[108]F.J.G.Clemente,G.M.P(?)rez,A.F.G.Skarmeta.An XML-seamless policy based management framework[C].Third International Workshop on Mathematical Methods,Models,and Architectures for Computer Network Security,MMM-ACNS 2005,Russia,September 2005.
[109]N.Samaan,A.Karmouch.An automated policy-based management framework for differentiated communication systems[J].IEEE Jounal on Selected Areas in Communications,2005,23(12):2236-2247.
[110]张少俊,李建华,郑明磊.基于策略的网络管理[J].计算机工程,2003,29(16):127-129.
[111]李金平,高东杰.基于策略的网络管理系统软件平台的研究及实现[J].计算机工程与应用,2002,38(12):177-179.
[112]E.C.Lupu,M.Sloman.Conflicts in policy-based distributed systems management[J].IEEE Transactions on Software Engineering,1999,25(6):852-869.
[113]A.Ponnappan,L.J.Yang,R.R.Pillai,et al.A policy based QoS management system for the IntServ/DiffServ based Interaet[C].Proceeding of the Third International Workshop on Policies for Distributed Systems and Networks (POLICY'02),2002,159-168.
[114]J.Hayes.Policy-based authentication and authorization:secure access to the network infrastructure[C].16th Annual Conference of the Computer Security Applications(ACSAC'00),2000,328-333.
[115]韩菊松,许华虎,余俊.基于策略的网络QoS管理[J].计算机工程,2004,30(8):114-116.
[116]袁满,胡建平,何锐等.基于策略的网格应用服务QoS管理模型[J].北京航空航天大学,2005,31(6):700-704.
[117]赵海萍,赵东风.基于Web2K服务器的QoS调度策略分析[J].计算机工程与应用,2005,41(13):125-128.
[118]李冬,汪东升,王耀才.一种QoS保证的存储虚拟化策略[J].计算机工程与应用,2005,41(11):15-25.
[119]H.J.Lim,K.J.Chun,T.M.Chung.Adaptive bandwidth control using fuzzy inference in policy-based network management[C].Information Networking:Convergence in Broadband and Mobile Networking(ICOIN 2005),LNCS 3391, 2005,443-451.
[120]张新跃,邓炜春,沈树群.策略驱动的网络安全管理模型[J].计算机应用研究,2005,22(1):226-231.
[121]张文凯,曹元大.应用安全平台体系中安全策略模型的研究[J].计算机工程,2005,31(1):136-162.
[122]姚键,孙昌平,孙虎等.基于策略的安全管理研究[J].计算机应用与软件,2005,22(3):115-119.
[123]李小平,吴琼,董庆宽等.IPv6网络中多级多域安全策略系统研究[J].西安电子科技大学学报,2008,35(2):300-304.
[124]陈卓.基于形式化与图形化的IPSec安全策略管理[J].计算机工程,2007,33(5):123-125.
[125]董守玲,谭彦,张凌.一个通用可扩展的网络安全策略管理系统[J].2005,45(增刊):172-175.
[126]李汉菊,张辉艳,李之棠.基于CORBA的分布式VPN策略管理的研究[J].2005,33(6):1-3.
[127]张文凯,曹元大.应用安全平台体系中安全策略模型的研究[J].2005,31(1):136-138.
[128]侯洪志.基于策略的QoS网络管理系统中PDP的研究与设计[D].长春:吉林大学,2004.
[129]金铭.基于策略的QoS网络管理系统中COPS协议研究与实现[D].长春:吉林大学,2004.
[130]B.Moore.Policy core information model extensions[S].RFC 3460,January 2003.
[131]王玢,左明雪.人体及动物生理学[M].高等教育出版社,2001.
[132]杨贝贝,阮晓刚.基于尖峰神经元的条件反射模型及其认知行为的研究[J].系统仿真学报,2005,17(9):2134-2137.
[133]R.A.Rescorla,A.R.Wagner.A theory of Pavlovian conditioning:variations in the efectiveness of reinforcement and nonreinforecement[C].Classical Conditioning II:current research and theory,New York,1972,64-99.
[134]A.R.Wagner.SOP:a model of automatic memory processing in animal behavior[C].Information Processing in Animals:Memory Mechanisms,Erlbaum,New Jersey,1981,85,5-44.
[135]R.S.Sutton,A.G Barto.Toward a modern theory of adaptive networks:expectation and prediction[J].Psychological Review,1981,88(2):135-170.
[136]R.S.Suton,A.G Barto.Time-derivative models of pavlovian reinforcement[M].Learning and computational neuronscience:foundations of adaptive networks,Cambridge,MA:MIT Press,1990.
[137]R.S.Suton,A.G Barto.Reinforcement learning:an introduction[M].Cambridge,MA:MIT Press,1998.
[138]A.H.Klopf.The hedonistic neuron:a theory of memory,learning and intelligence[M].Washinton D C,Hemisphere,1982.
[139]A.H.Klopf.A neuronal model of classical conditioning[J].Psychobiology,1988,16(2):85-125.
[140]G Stephen,A.S.Nestor.Neural dynamics of adaptive timing and temporal discrimination during associative learning[J].Neural Networks,1989,2(2):79-102.
[141]N.A.Schmajuk,J.J.DiCarlo.Stimulus configuration,classical conditioning,and hippocampal function[J].Psychological Review,1992,99(1):268-305.
[142]N.A.Schmajuk.Animal Learning and Cognition:A Neural Network Approach[M].Cambridge University Press,1997.
[143]C.Balkenius.Natural intelligence in artificial creatures[D].Lund University Cognitive Studies 37,1995.
[144]C.Balkenius.A neural network model of classical conditioning I:The dynamics of learning[D].Lund University Cognitive Studies 68,1998.
[145]C.Balkenius,J.Moren.Dynamics of a Classical Conditioning Model[J].Autonomous Robots,1999,7(1):41-56.
[146]R.Malaka,R.Lange,M.Hammer.A comparative study of real-time models for classical conditioning[C].Learning and Memory:Proceedings of the 23rd Gottingen Neurobiology Conference,Stuttgart,New York,1995,74-80.
[147]R.Malaka,R.Lange,M.Hammer.A constant prediction model for classical conditioning[C].Learning and Memory:Proceedings of the 23rd Gotten Neurobiology Conference,Stuttgart,New York,1995,75-80.
[148]A.Mignault,A.A.J.Marley,A real-time neuronal model of classical conditioning [J].Adaptive Behavior,1997,6(1):3-61.
[149]J.Christopher,L.Anders.An associative neural network model of classical conditioning[R].TRITA-NA-P0217,2002.
[150]T.Yamazaki,S.Tanaka.A neural network model for trace conditioning[J].International Journal of Neural Systems,2005,15(1 & 2):23-30.
[151]杨贝贝,阮晓刚.经典条件反射认知模型及其在运动控制系统中的应用[D].北京:北京工业大学,2005年.
[152]顾冬雷,陈卫东,席裕庚.移动机器人条件反射能力的实现[J].机器人,2001,23(2):123—126.
[153]R.I.Damper,R.L.B.French,T.W.Scutt.ARBIB:an Autonomous Robot Based on Inspirations from Biology[J].Robotics and Autonomous Systems,2000,31(4):247-274.
[154]胡新宇,赵明富,余成波等.机械定位控制的条件反射感知控制方法研究[J].中国制造业信息化,2004,33(10):111-113.
[155]L.C.Daniel,A comparison of some neural network models of classical conditioning[C].Intelligent Control,Proceedings 5th IEEE International Symposium,Philadelphia,PA,1990,1163-1168.
[156]R.Malaka,M.Hammer.Real-time models of classical conditioning[C].Neural Networks,Proceedings of IEEE International Conference,Washington DC,USA,1996,768-773.
[157]R.Malaka.Models of classical conditioning[J].Bulletin of Mathematical Biology,1999,61(1):33-83.
[158]C.R.Gallistel,J.Gibbon.Computational Versus Associative Models of Simple Conditioning[J].Current Directions in Psychological Science,2001,10:146-150.
[159]赵季红.下一代网络的网络管理技术[J].西安邮电学院学报,2004,9(1):1-5.
[160]S.Hahn,K.Chan,K.MCloghrie.Framework Policy Information Base[S].RFC 3318,March 2003.
[161]K.Chan,R.Sahita,S.Hahn,et al.Differentiated services quality of service policy information base[S].RFC 3317,March 2003.
[162]D.Rawlins,A.Kulkarni,et al.Framework policy information base for usage feedback[S].RFC3571,August 2003.
[163]R.M.Habib,M.S.Hasan,L.Caro.A new genetic algorithm with Lamarckian individual learning for generation scheduling[J].IEEE Transactions on Power Systems,2003,18(3):1181-1186.
[164]阎岭,蒋静坪.进化学习策略收敛性和逃逸能力的研究[J].自动化学报,2005,31(6):873-880.
[165]王洪峰,汪定伟,杨圣祥.动态环境中的进化算法[J].控制与决策,2007,22(2):127-131,137.
[166]程博,郭振宇,王军平等.一种并行免疫进化策略算法研究[J].控制与决策,2007,22(12):1395-1398,1402.
[167]任峡,吕述望.ARP协议欺骗原理分析与抵御方法[J].计算机工程,2003,29(9):127-182.
[168]徐丹,黎俊伟,高传善.基于ETHERNET的网络监听以及ARP欺骗[J].计算机应用与软件,2005,22(11):105-107.
[169]李海鹰,程灏,吕志强等.针对ARP攻击的网络防御模式设计与实现[J].计算机工程,2005,31(5):170-171.
[170]李艇.网络管理技术与应用[M].北京:高等教育出版社,2003.
[171]AdventNet Inc.Java SNMP API Documentation[EB/OL].http://snmp.adventnet. com,2005.
[172]刘琦,李建华.网络内容安全监管系统的框架及其关键技术[J].计算机工程,2003,29(2):287-289.
[173]杨经,司志刚,叶惠敏.基于内容安全的局域网监控系统的设计与实现[J].计算机工程与应用,2002,38(19):174-176,200.
[174]赵仲孟,陈剑,张选平等.网络内容安全分析与对策[J].计算机工程,2002,28(5):41—43.
[175]周澔宇.基于URL的网页内容过滤器的设计与实现[J].计算机工程,2006,32(7):81-83.
[176]M.Hammami,Y.Chahir,L.M.Chen.WebGuard:web based adult content detection and filtering System[C].Proceedings of the IEEE/WIC International conference on web Intelligence(WI'03),Halifax,Canada,2003,574-578.
[177]彭伟.使用802.1x实现校园网认证[J].计算机应用,2003,23(3):85-87.
[178]朱海龙,张国清.基于802.1x的以太网接入技术[J].计算机工程,2003,29(18):130-132.
[179]高祥,周林.802.1x协议及其在宽带接入中的应用[J].重庆邮电学院学报,2004,16(1):91-97.
[180]蒋海峰,苗放.802.1x标准和Radius协议的扩展应用研究[J].计算机应用,2003,23(11):88-90.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |