普适计算中智能服务选择算法研究
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摘要
普适计算是信息空间与物理空间的融合,在这个融合的空间中人们可以随时随地和透明地获得数字化的服务,实现计算机本身从人们的视线中消失,人们注意力的中心可以回归到要完成的任务本身。因此需要提供一种对设备服务进行自动发现和选择的机制,设备之间进行自主交互,并对用户透明,这是普适计算研究的主要目标之一。
在普适计算系统中,由于计算复杂性和移动性的增加,需要多个设备协同在一起来共同完成任务,因此,如何在若干个可供选择的服务中进行选择是很重要的,是实现普适计算目标的关键一步。针对传统计算模式中的服务选择方法只能根据功能对服务进行选择的问题,服务请求者和提供者是一种紧密耦合的关系,并没有考虑服务的上下文信息以及影响功能的其它因素,使服务选择具有盲目性和随意性,导致系统性能的下降。本文从服务发现与选择的基础开始,研究普适计算中的智能服务选择和服务迁移方法。提出了普适计算环境下基于神经网络的智能服务选择模型(ANNSSM)和算法(ANNSSBP),设计了普适计算环境下面向服务的无缝迁移(SOSM)算法,并通过理论分析和仿真来比较和评估所提出方案的效果和性能,并最终搭建一个能够验证上述算法的实验原型系统。
本文的主要研究工作和创新成果可以总结概括为如下三个方面:
第一,提出了普适计算环境下一种新的基于神经网络的智能服务选择系统模型框架,并设计普适计算环境下基于神经网络的服务选择算法,这种算法以设备之间合作的先验知识和现场上下文信息为基础,采用神经网络预测控制器,根据所要寻找服务的多属性来对目标服务集中的对象进行评估和预测,给出对目标服务的衡量标准,最终在服务相似集中选择出最符合用户要求的服务。ANNSSBP算法的提出使得服务选择过程智能化,并使得用户与服务之间的紧密耦合关系发展到松散耦合关系,通过服务自适应选择框架,提高服务选择成功率,提升系统服务效率和质量。
第二,提出了自适应神经网络结构调整新方法,设计了基于三因素(学习率因子LR,动量项因子MF和比例项因子PF)的BP神经网络改进算法,通过自适应合并或删除冗余节点优化神经网络结构,并采用限定影响神经网络收敛性的有关因素比例关系的方法来避免迭代过程中的局部最优问题,使得改进的神经网络算法具有较高的收敛速度和良好的收敛稳定性,以适应普适计算环境的新特性。
第三,提出了普适计算环境下一种新的针对不同的服务失效类型的面向服务的无缝迁移算法,设计触发服务迁移的条件和判定定理、迁移成功或失败的评价方法和判定定理,研究对触发服务迁移有影响的迁移点恢复正确率、服务重启残余依赖度、迁移时延满足率等问题,并给出相应的解决策略了,其中重点研究并设计了服务迁移路径优化策略。SOSM算法的提出使得服务可以在系统中无缝迁移,从全局的角度优化计算资源,提高服务选择系统的可靠性。
总之,普适计算环境下基于神经网络的智能服务选择和迁移模型研究,是一种从智能控制的角度进行服务选择和迁移控制的新策略,具有较高的研究和应用价值。由于它能充分考虑环境的动态变化和上下文信息对服务选择的影响,因而能提高服务选择的成功率、降低服务选择的盲目性,提高系统的可靠性,达到改善系统性能的目的。研究和开发智能服务选择系统对于推动普适计算研究具有积极的现实意义。
本项研究工作受到东华大学博士学位论文创新基金项目“普适计算环境中智能服务选择算法研究”和教育部科学技术研究重点基金项目“普适计算中位置感知研究”的资助。
Ubiquitous computing makes it possible to integrate the physical world we are living in and the virtual world in the information space together as the whole,where the users can expediently and transparently obtain digital services.It would enable people to focus on their task itself,instead of the computer,so as to make the computer invisible from users' sight.Therefore, it is necessary to provide a kind of mechanism that users can find and select devices or services.The process must be transparently completed to users, which is also one of the main goals for ubiquitous computing.
In ubiquitous computing system,because of the increasing of computing complexity and mobility,there must be more than one device to cooperate to finish the target task.So how to choose a suitable service from all the useable service candidates is the most important step for ubiquitous computing.For the problem that in traditional computing model the users only select service according to their function of services,the relation between user and service provider is close coupling and it didn't consider that context information and other correlative factor severely affect service selection activity.There is not an effective guidance provided to users,so the selecting process is blindly and arbitrarily implemented.Its can be the results of low system performance and bad efficiency.In this thesis, beginning from the basics of service discovery and selection,we research the intelligent method to select the optimum service and migrate the failure service.Then,we put forward a novel ANN-based intelligent service selection model(ANNSSM) and algorithm(ANNSSBP) for ubiquitous computing;furthermore,we propose a novel service-oriented seamless migration(SOSM) algorithm for ubiquitous computing.We evaluate performance and effort of proposed project by adopting method of theory analysis and simulation.Finally,we develop a prototype system to testify the validity of proposed algorithms.
The main contents and contributions of our work are as following:
Firstly,we put forward the architecture of ANN-based(Artificial Neural Networks) intelligent service selection system for ubiquitous computing,and propose a novel ANN-based intelligent service selection algorithm for ubiquitous computing.This algorithm is based on the earlier knowledge of information of the cooperation among devices and the context information of ubiquitous computing environment.At the same time,in our novel model,the Neural Networks Prediction Controller is adopted,and an evaluation about the target service is given out according to the properties of the services.Consider that the evaluation value can give out a guidance standard,so user can choose a most suitable service from many service candidates.The suitable service can provide the most perfect performance to user.Because of adopting the novel ANNSSBP algorithm,the process of service selecting is intelligentized,and it makes that the relation between user and service provider is changed from close coupling to loose coupling. By adopting this kind of self-adapting architecture of service selection system,the system efficiency can be observably improved and the Quality of Service(QoS) is observably improved.
Secondly,we put forward the novel method of self-adjusting architecture of Artificial Neural Networks,and improve BP algorithm based on three-term(namely Learning Rate,Momentum Factor and Proportional Factor,shortly LR,MF and PF).In order to optimizing the architecture of Artificial Neural Networks,the self-adjusting architecture method is adopted by uniting or cutting down nodes of network so that a moderate scale of neural network can be obtained.At the same time,we adapt the method of proportinal factor among all kind of correlative parameters to avoid tripping into local minimum in the course of iterative process.The convergence speed and stability of improved BP algorithm were enhanced by adding the proportional factor,which make it possible to satisfy the new characteristic of ubiquitous computing.
Thirdly,we put forward a new service-oriented seamless migration algorithm for all kind of failure service in ubiquitous computing environment,and propose the condition and judgement theorem of triggering service migration active,judgement theorem of successful service migration active.We research on some correlative problems and give out some simulations,including rate of correctly resuming for service checkpoint,rate of being gratified for migration delay,rate of remains dependent for service restarting and so on.One of these problems what we detailedly research is the mechanism of optimizing service migration route. The new SOSM algorithm can make service seamlessly migrate among the nodes of ubiquitous computing networks and optimize the global computing resource,which enhance the reliability of service system.
In a word,this thesis has proposed the novel ANN-based intelligent service selection model and service-oriented seamless migration model, which are new mechanism about service selection and migration from an intelligent-control's point of view.The new mechanism is peculiarly valuable for research and application.Because it fully consider the impact of context information and varying environment,it can enhance the rate of successful service selection active,decrease the blindly and arbitrarily service selecting process,and improve the system reliability so that system be optimizing.It is really valuable to research and develop intelligent service selection system in order to develope ubiquitous computing research.
This work is partially supported by the Innovational Thesis of Doctor Degree of Donghua University and the Ministry of Education Technology Research Key Foundation of China under grant.
在普适计算系统中,由于计算复杂性和移动性的增加,需要多个设备协同在一起来共同完成任务,因此,如何在若干个可供选择的服务中进行选择是很重要的,是实现普适计算目标的关键一步。针对传统计算模式中的服务选择方法只能根据功能对服务进行选择的问题,服务请求者和提供者是一种紧密耦合的关系,并没有考虑服务的上下文信息以及影响功能的其它因素,使服务选择具有盲目性和随意性,导致系统性能的下降。本文从服务发现与选择的基础开始,研究普适计算中的智能服务选择和服务迁移方法。提出了普适计算环境下基于神经网络的智能服务选择模型(ANNSSM)和算法(ANNSSBP),设计了普适计算环境下面向服务的无缝迁移(SOSM)算法,并通过理论分析和仿真来比较和评估所提出方案的效果和性能,并最终搭建一个能够验证上述算法的实验原型系统。
本文的主要研究工作和创新成果可以总结概括为如下三个方面:
第一,提出了普适计算环境下一种新的基于神经网络的智能服务选择系统模型框架,并设计普适计算环境下基于神经网络的服务选择算法,这种算法以设备之间合作的先验知识和现场上下文信息为基础,采用神经网络预测控制器,根据所要寻找服务的多属性来对目标服务集中的对象进行评估和预测,给出对目标服务的衡量标准,最终在服务相似集中选择出最符合用户要求的服务。ANNSSBP算法的提出使得服务选择过程智能化,并使得用户与服务之间的紧密耦合关系发展到松散耦合关系,通过服务自适应选择框架,提高服务选择成功率,提升系统服务效率和质量。
第二,提出了自适应神经网络结构调整新方法,设计了基于三因素(学习率因子LR,动量项因子MF和比例项因子PF)的BP神经网络改进算法,通过自适应合并或删除冗余节点优化神经网络结构,并采用限定影响神经网络收敛性的有关因素比例关系的方法来避免迭代过程中的局部最优问题,使得改进的神经网络算法具有较高的收敛速度和良好的收敛稳定性,以适应普适计算环境的新特性。
第三,提出了普适计算环境下一种新的针对不同的服务失效类型的面向服务的无缝迁移算法,设计触发服务迁移的条件和判定定理、迁移成功或失败的评价方法和判定定理,研究对触发服务迁移有影响的迁移点恢复正确率、服务重启残余依赖度、迁移时延满足率等问题,并给出相应的解决策略了,其中重点研究并设计了服务迁移路径优化策略。SOSM算法的提出使得服务可以在系统中无缝迁移,从全局的角度优化计算资源,提高服务选择系统的可靠性。
总之,普适计算环境下基于神经网络的智能服务选择和迁移模型研究,是一种从智能控制的角度进行服务选择和迁移控制的新策略,具有较高的研究和应用价值。由于它能充分考虑环境的动态变化和上下文信息对服务选择的影响,因而能提高服务选择的成功率、降低服务选择的盲目性,提高系统的可靠性,达到改善系统性能的目的。研究和开发智能服务选择系统对于推动普适计算研究具有积极的现实意义。
本项研究工作受到东华大学博士学位论文创新基金项目“普适计算环境中智能服务选择算法研究”和教育部科学技术研究重点基金项目“普适计算中位置感知研究”的资助。
Ubiquitous computing makes it possible to integrate the physical world we are living in and the virtual world in the information space together as the whole,where the users can expediently and transparently obtain digital services.It would enable people to focus on their task itself,instead of the computer,so as to make the computer invisible from users' sight.Therefore, it is necessary to provide a kind of mechanism that users can find and select devices or services.The process must be transparently completed to users, which is also one of the main goals for ubiquitous computing.
In ubiquitous computing system,because of the increasing of computing complexity and mobility,there must be more than one device to cooperate to finish the target task.So how to choose a suitable service from all the useable service candidates is the most important step for ubiquitous computing.For the problem that in traditional computing model the users only select service according to their function of services,the relation between user and service provider is close coupling and it didn't consider that context information and other correlative factor severely affect service selection activity.There is not an effective guidance provided to users,so the selecting process is blindly and arbitrarily implemented.Its can be the results of low system performance and bad efficiency.In this thesis, beginning from the basics of service discovery and selection,we research the intelligent method to select the optimum service and migrate the failure service.Then,we put forward a novel ANN-based intelligent service selection model(ANNSSM) and algorithm(ANNSSBP) for ubiquitous computing;furthermore,we propose a novel service-oriented seamless migration(SOSM) algorithm for ubiquitous computing.We evaluate performance and effort of proposed project by adopting method of theory analysis and simulation.Finally,we develop a prototype system to testify the validity of proposed algorithms.
The main contents and contributions of our work are as following:
Firstly,we put forward the architecture of ANN-based(Artificial Neural Networks) intelligent service selection system for ubiquitous computing,and propose a novel ANN-based intelligent service selection algorithm for ubiquitous computing.This algorithm is based on the earlier knowledge of information of the cooperation among devices and the context information of ubiquitous computing environment.At the same time,in our novel model,the Neural Networks Prediction Controller is adopted,and an evaluation about the target service is given out according to the properties of the services.Consider that the evaluation value can give out a guidance standard,so user can choose a most suitable service from many service candidates.The suitable service can provide the most perfect performance to user.Because of adopting the novel ANNSSBP algorithm,the process of service selecting is intelligentized,and it makes that the relation between user and service provider is changed from close coupling to loose coupling. By adopting this kind of self-adapting architecture of service selection system,the system efficiency can be observably improved and the Quality of Service(QoS) is observably improved.
Secondly,we put forward the novel method of self-adjusting architecture of Artificial Neural Networks,and improve BP algorithm based on three-term(namely Learning Rate,Momentum Factor and Proportional Factor,shortly LR,MF and PF).In order to optimizing the architecture of Artificial Neural Networks,the self-adjusting architecture method is adopted by uniting or cutting down nodes of network so that a moderate scale of neural network can be obtained.At the same time,we adapt the method of proportinal factor among all kind of correlative parameters to avoid tripping into local minimum in the course of iterative process.The convergence speed and stability of improved BP algorithm were enhanced by adding the proportional factor,which make it possible to satisfy the new characteristic of ubiquitous computing.
Thirdly,we put forward a new service-oriented seamless migration algorithm for all kind of failure service in ubiquitous computing environment,and propose the condition and judgement theorem of triggering service migration active,judgement theorem of successful service migration active.We research on some correlative problems and give out some simulations,including rate of correctly resuming for service checkpoint,rate of being gratified for migration delay,rate of remains dependent for service restarting and so on.One of these problems what we detailedly research is the mechanism of optimizing service migration route. The new SOSM algorithm can make service seamlessly migrate among the nodes of ubiquitous computing networks and optimize the global computing resource,which enhance the reliability of service system.
In a word,this thesis has proposed the novel ANN-based intelligent service selection model and service-oriented seamless migration model, which are new mechanism about service selection and migration from an intelligent-control's point of view.The new mechanism is peculiarly valuable for research and application.Because it fully consider the impact of context information and varying environment,it can enhance the rate of successful service selection active,decrease the blindly and arbitrarily service selecting process,and improve the system reliability so that system be optimizing.It is really valuable to research and develop intelligent service selection system in order to develope ubiquitous computing research.
This work is partially supported by the Innovational Thesis of Doctor Degree of Donghua University and the Ministry of Education Technology Research Key Foundation of China under grant.
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