新一代互联网服务及故障检测若干关键技术的研究
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摘要
服务的可管理性是新一代互联网服务的核心问题之一。可以预见互联网上的服务将与时俱增,如果没有科学的方法对服务进行管理,互联网的服务将不可控。如何形式化描述和语义化表示服务的内部结构及接口,如何使用元服务组合成功能更强的高级服务,如何动态查找和匹配服务,如何描述故障检测服务的属性及性能指标,如何有效部属故障检测服务,如何评估故障检测服务的性能,如何提高故障检测服务的性能,这些是服务管理必须解决的理论问题。以解决这一关键的科学问题为目标,本文采用理论分析和实验相结合的方式重点针对新一代互联网服务及故障检测的若干关键技术进行研究并取得了一系列有价值的成果。概括起来,本论文的创新成果主要体现在以下几个方面:
1)分析了下一代互联网基础网络资源异构、用户需求个性化、参与者众多等特征;研究了WEB服务、语义WEB、服务分类、参与者角色细分、面向服务的体系结构、开放网格服务体系结构等研究成果;从服务参与者、语义描述、技术视点分别提出了多角色合作交互管理模型、本体模型和能力分层提供模型。多角色合作交互管理模型使用智能化方式综合管理服务;本体模型解决了服务本质特征统一语义描述的问题;能力分层提供模型实现了异构基础网络资源的融合开放;基于三个服务模型提出了一个完整的服务框架。解决网络体系结构的相对稳定性和复杂多变的用户服务需求之间的基本矛盾。
2)分析了网格环境下故障检测服务所需满足的各种要求,形式化的描述网格故障模型、故障检测器模型及性能评测指标;提出一个多层故障检测机制,能根据不同网格环境(节点内、站点、虚拟组织),实施不同的故障检测机制,在保证完整性、可靠性、动态性、可扩展性、适应性、多样性等需求的前提下,能及时有效地检测出进程、主机、站点等不同层次的故障。
3)研究分析了网格等新一代互联网服务必须提供的另一个重要指标-互操作性,首先利用“本体”描述故障检测服务,然后利用面向服务的思想使多级故障检测实现不依赖于具体的环境,保证了互操作性要求。
4)利用大量的仿真实验研究心跳式故障检测器在无线自组网不同路由协议上的性能,提出以变化节点数量、节点移动速度、节点业务量、节点传输范围来评估性能。仿真结果显示主动路由提供较好的平均延时、平均故障检测时间,被动路由提供较好的标准化路由开销、能量消耗率、交付率、误检测率。仿真结果可以为无线自组网环境下的故障检测器提供参考。
5)为了使故障检测服务能精确地预测心跳消息的延时,提出一种新颖的黑箱模型预测下一个心跳消息的到达时间。将心跳消息到达时间看作自回归过程,将心跳消息发送时间看作外生变量,基于滑动窗口的观测值用来估算模型系数,然后使用这个结果估算下一个心跳消息到达时间。仿真实验表明,在瓶颈链路、链路失效恢复、可用带宽变化、心跳消息间隔变化等不同的网络环境下,自适应自回归外生变数模型能适应这些变化,精确捕获心跳消息的到达时间,与传统的时间序列预测方法相比,产生最小的预测错误。
6)使用两层前向神经网络学习并近似心跳消息的非线性特征,精确地预测心跳消息的延时。输入包含一个滑动窗口心跳消息延时的观测值,输出是提前一步预测,首先使用后向传播算法来训练网络节点的权值、偏置值,然后使用最速梯度下降规则调整。仿真实验表明,自适应自回归外生变数模型能适应这些变化,精确捕获心跳消息到达时间,产生更好的预测结果。
7)使用非线性自回归外生变数网络来学习心跳消息的线性、非线性特征,进而实现提前一步预测。输入包含一个滑动窗口心跳消息到达的观测值和一个滑动窗口的心跳消息发送时间,输出是提前一步预测,首先使用后向传播算法来训练网络节点的权值、偏置值,然后使用最速梯度下降规则调整。仿真实验表明,这种自适应算法能精确捕获心跳消息到达时间,产生最好的预测结果。
Services namagement is one of the core issues for new generation of Internet service. Internet services will not be controllable in future if there is no scientific method to manage services. How to formally describe internal structure and interface of service, how to assemble more advanced services, how to find and match service on demand, how to describe the properties and performance metrics of failure detection service, how to effectively deploy failure detection services, how to evaluate the performance of failure detection service, how to improve the performance of failure detection services, all these theoretical issues must be resolved.
To address above critical scientific problems, this dissertation mainly focuses on several key technologies of new generation Internet service and fault detection with help of theoretical analysis and experimental simulations. The main contributions are summarized as follow:
1) We analyze the characteristics of the heterogeneous network infrastructure, personalized user requirements, many participants for new generation Internet. We propose three service models based on the views of participators, semantic description and implementation. These models provide uniform, effective, intelligent mechanisms to manage, describe, implement NGI service. A hierarchical architecture is introduced to direct further NGI service management research。
2) We first analyze the various requirements for Grid failure detection services, formal description of the failure model, failure detector model as well as performance evaluation metrics. Then propose multi-level failure detectors which can implement different failure detection mechanisms based on different Grid environment (nodes, the sites, virtual organizations) to detect the failures of process, host, site respectively and guarantee all Grid requirements.
3) Interoperability is another important property which must be guaranteed for failure detection in Grid. We use ontology to describe failure detection service and propose a service-oriented architecture framework to make it to be independent of the specific implementation and guarantee its interoperability.
4) We do the comprehensive simulations to investigate the performance of heartbeat-style failure detector over proactive and reactive routing protocols with simulator. The performance different are analyzed using varying the number of nodes, mobility speed, traffic load, transmission range. Simulation shows that proactive performed better in average delay and average failure detection time, reactive performed better in normalized routing overhead, delivery ratio, energy consumption ratio, false detection ratio. The observations can be used to motivate and improve future implementations of failure detection service in Ad hoc wireless networks.
5) Internet dynamic characters make it very difficult to understand message behavior and accurately predict heartbeat arrival time. To overcome this problem, a novel black-box model is proposed to predict next heartbeat arrival time. Heartbeat arrival time is modeled as Auto-Regressive process, heartbeat sent time is modeled as exogenous variable, the coefficients are estimated based on the sliding window of observations and this result is used to predict next heartbeat arrival time. Simulation shows this adaptive Auto-Regressive exogenous (ARX) model can accurately capture heartbeat arrival dynamics and make minimum prediction error under different network environments.
6) Two-layer feed forward neural network is proposed to learn nonlinear characters of heartbeat messages, perform one-step-ahead prediction to estimate future heartbeat message delay. Inputs are one moving window of observations of the heartbeat delays, output is the one-step-ahead future value, the neural network is trained by back-propagation algorithm, its weights and basis are adjusted by approximate steepest descent rule. Simulation shows this adaptive algorithm can accurately capture heartbeat message dynamics and make better prediction result.
7) We use a nonlinear autoregressive network with exogenous inputs to learn nonlinear and linear characters of heartbeat messages, perform one-step-ahead prediction to estimate future heartbeat delay. The inputs are two moving window observations of past heartbeat delays and heartbeat sending time, the output is next heartbeat delay, the network is trained by standard back-propagation algorithm, its weights and basis are adjusted by approximate steepest descent rule. Simulation shows this adaptive algorithm can accurately capture heartbeat dynamics over internet and make best prediction result.
1)分析了下一代互联网基础网络资源异构、用户需求个性化、参与者众多等特征;研究了WEB服务、语义WEB、服务分类、参与者角色细分、面向服务的体系结构、开放网格服务体系结构等研究成果;从服务参与者、语义描述、技术视点分别提出了多角色合作交互管理模型、本体模型和能力分层提供模型。多角色合作交互管理模型使用智能化方式综合管理服务;本体模型解决了服务本质特征统一语义描述的问题;能力分层提供模型实现了异构基础网络资源的融合开放;基于三个服务模型提出了一个完整的服务框架。解决网络体系结构的相对稳定性和复杂多变的用户服务需求之间的基本矛盾。
2)分析了网格环境下故障检测服务所需满足的各种要求,形式化的描述网格故障模型、故障检测器模型及性能评测指标;提出一个多层故障检测机制,能根据不同网格环境(节点内、站点、虚拟组织),实施不同的故障检测机制,在保证完整性、可靠性、动态性、可扩展性、适应性、多样性等需求的前提下,能及时有效地检测出进程、主机、站点等不同层次的故障。
3)研究分析了网格等新一代互联网服务必须提供的另一个重要指标-互操作性,首先利用“本体”描述故障检测服务,然后利用面向服务的思想使多级故障检测实现不依赖于具体的环境,保证了互操作性要求。
4)利用大量的仿真实验研究心跳式故障检测器在无线自组网不同路由协议上的性能,提出以变化节点数量、节点移动速度、节点业务量、节点传输范围来评估性能。仿真结果显示主动路由提供较好的平均延时、平均故障检测时间,被动路由提供较好的标准化路由开销、能量消耗率、交付率、误检测率。仿真结果可以为无线自组网环境下的故障检测器提供参考。
5)为了使故障检测服务能精确地预测心跳消息的延时,提出一种新颖的黑箱模型预测下一个心跳消息的到达时间。将心跳消息到达时间看作自回归过程,将心跳消息发送时间看作外生变量,基于滑动窗口的观测值用来估算模型系数,然后使用这个结果估算下一个心跳消息到达时间。仿真实验表明,在瓶颈链路、链路失效恢复、可用带宽变化、心跳消息间隔变化等不同的网络环境下,自适应自回归外生变数模型能适应这些变化,精确捕获心跳消息的到达时间,与传统的时间序列预测方法相比,产生最小的预测错误。
6)使用两层前向神经网络学习并近似心跳消息的非线性特征,精确地预测心跳消息的延时。输入包含一个滑动窗口心跳消息延时的观测值,输出是提前一步预测,首先使用后向传播算法来训练网络节点的权值、偏置值,然后使用最速梯度下降规则调整。仿真实验表明,自适应自回归外生变数模型能适应这些变化,精确捕获心跳消息到达时间,产生更好的预测结果。
7)使用非线性自回归外生变数网络来学习心跳消息的线性、非线性特征,进而实现提前一步预测。输入包含一个滑动窗口心跳消息到达的观测值和一个滑动窗口的心跳消息发送时间,输出是提前一步预测,首先使用后向传播算法来训练网络节点的权值、偏置值,然后使用最速梯度下降规则调整。仿真实验表明,这种自适应算法能精确捕获心跳消息到达时间,产生最好的预测结果。
Services namagement is one of the core issues for new generation of Internet service. Internet services will not be controllable in future if there is no scientific method to manage services. How to formally describe internal structure and interface of service, how to assemble more advanced services, how to find and match service on demand, how to describe the properties and performance metrics of failure detection service, how to effectively deploy failure detection services, how to evaluate the performance of failure detection service, how to improve the performance of failure detection services, all these theoretical issues must be resolved.
To address above critical scientific problems, this dissertation mainly focuses on several key technologies of new generation Internet service and fault detection with help of theoretical analysis and experimental simulations. The main contributions are summarized as follow:
1) We analyze the characteristics of the heterogeneous network infrastructure, personalized user requirements, many participants for new generation Internet. We propose three service models based on the views of participators, semantic description and implementation. These models provide uniform, effective, intelligent mechanisms to manage, describe, implement NGI service. A hierarchical architecture is introduced to direct further NGI service management research。
2) We first analyze the various requirements for Grid failure detection services, formal description of the failure model, failure detector model as well as performance evaluation metrics. Then propose multi-level failure detectors which can implement different failure detection mechanisms based on different Grid environment (nodes, the sites, virtual organizations) to detect the failures of process, host, site respectively and guarantee all Grid requirements.
3) Interoperability is another important property which must be guaranteed for failure detection in Grid. We use ontology to describe failure detection service and propose a service-oriented architecture framework to make it to be independent of the specific implementation and guarantee its interoperability.
4) We do the comprehensive simulations to investigate the performance of heartbeat-style failure detector over proactive and reactive routing protocols with simulator. The performance different are analyzed using varying the number of nodes, mobility speed, traffic load, transmission range. Simulation shows that proactive performed better in average delay and average failure detection time, reactive performed better in normalized routing overhead, delivery ratio, energy consumption ratio, false detection ratio. The observations can be used to motivate and improve future implementations of failure detection service in Ad hoc wireless networks.
5) Internet dynamic characters make it very difficult to understand message behavior and accurately predict heartbeat arrival time. To overcome this problem, a novel black-box model is proposed to predict next heartbeat arrival time. Heartbeat arrival time is modeled as Auto-Regressive process, heartbeat sent time is modeled as exogenous variable, the coefficients are estimated based on the sliding window of observations and this result is used to predict next heartbeat arrival time. Simulation shows this adaptive Auto-Regressive exogenous (ARX) model can accurately capture heartbeat arrival dynamics and make minimum prediction error under different network environments.
6) Two-layer feed forward neural network is proposed to learn nonlinear characters of heartbeat messages, perform one-step-ahead prediction to estimate future heartbeat message delay. Inputs are one moving window of observations of the heartbeat delays, output is the one-step-ahead future value, the neural network is trained by back-propagation algorithm, its weights and basis are adjusted by approximate steepest descent rule. Simulation shows this adaptive algorithm can accurately capture heartbeat message dynamics and make better prediction result.
7) We use a nonlinear autoregressive network with exogenous inputs to learn nonlinear and linear characters of heartbeat messages, perform one-step-ahead prediction to estimate future heartbeat delay. The inputs are two moving window observations of past heartbeat delays and heartbeat sending time, the output is next heartbeat delay, the network is trained by standard back-propagation algorithm, its weights and basis are adjusted by approximate steepest descent rule. Simulation shows this adaptive algorithm can accurately capture heartbeat dynamics over internet and make best prediction result.
引文
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