基于马尔可夫过程的云服务组合QoS量化评估方法研究

针对云服务组合的QoS量化评估方法进行了研究。基于组合云服务环境中虚拟动态性和服务随机性的特点,提出了基于BPEL流程的云服务组合实现框架;在随机Petri网理论的基础上建立了组合云服务流程网模型(CCSPNet),进而应用马尔可夫过程对其进行性能评估,提出了云服务组合的六维QoS评价体系,并结合CCSPNet模型提出了云服务组合的QoS定量评估方法。通过应用实例分析表明,提出的方法具有较好的动态适应性和灵活性,能有效满足云服务应用环境的QoS评估需求。     

Web服务组合的可靠性动态评估模型*

为了合理、高效、动态地评估Web服务组合的可靠性,为服务请求者提供高质量的组合服务,提出了一个Web服务组合的可靠性动态评估模型。该模型对服务提供者发布至UDDI注册中心的Web服务进行语义预先处理,根据语义Web服务间的逻辑组合关系,基于预推理技术构造Web服务的自动组合框架,提出了Web服务的自动组合算法,建立Web服务组合方案的路径结构;利用随机Petri网对满足服务请求者需求的服务组合路径结构进行可靠性建模,结合在线获取的Web服务可靠性信息,对Web服务组合的可靠性进行动态评估。实验示例结果分析表明,提出的模型能确保Web服务组合方案的有效性和提高服务组合的效率,对Web服务组合的可靠性评估具有较强动态性和灵活适应性。     

云服务可靠性优化方法研究

针对云服务的冗余特性和可靠性保障的需求,探讨了提高云服务可靠性的有效途径。基于云服务的可靠性体系框架和管理模式,提出了信任冗余的云服务可靠性增强总体框架;在服务准备时的冗余设计阶段,基于选举协议的云服务轮询检测机制,设计了信任感知的容错服务选择算法,给出了最小容错服务个数的求解方法;基于服务组合运行时的容错处理框架,提出了保证服务响应时间的基于失效规则的云服务调用策略。实验结果表明,提出的容错服务选择算法和云服务调用策略具有较好的实用性和有效性。     

基于Petri网的服务可靠性评价方法研究

服务的可靠性研究成为一个研究热点。首先研究基于Petri网的结构关联的服务组合描述语言BPEL;而对于服务交互关联模型,构造服务组合模型的Petri网表示的交互特点;给出服务组合Petri网定义,建立基于服务组合Petri网的模型可靠性评估方法,包含结构关联的可靠性计算方法和交互关联模型可靠性化简和计算方法;最后给出旅游服务系统案例分析,说明了方法的可用性和合理性。     

基于TCPN的LBS动态服务组合建模与验证

针对位置服务的动态服务组合过程中对位置的动态性和实时性考虑少的问题,本文运用时间约束Petri网对位置服务进行动态服务组合建模,设计了位置服务动态服务组合模型的执行时间、计算方法和库所可调度性分析算法.并对该模型的可达性进行了验证,同时引入实例说明了运用时间约束Petri网对位置服务动态服务组合建模方法的可靠性和可行性.     

一种基于逻辑Petri网的Web服务簇组合方法

Web服务组合是实现快速服务增值和软件重用的重要方式,但现有的静态服务组合和动态服务组合方式都有待于进一步完善.文中集成静态服务组合和动态服务组合的优点,提出了一种在虚拟层面上基于服务簇进行服务组合的思想;研究并提出了一种基于服务簇的服务组合方法,并应用逻辑Petri网对其进行形式化建模描述;给出了服务簇网的基本组合模型,并分别对其完备性继承作了分析;研究了服务簇网组合的代数运算性质.最后,通过实验证明了服务簇网组合运算的可行性和有效性.     

基于OPN的指控能力包服务动态组合方法研究

服务组合方法对于分布式网络环境下基于SOA的军事应用系统的综合性能影响很大.提出了一种基于OPN的服务动态组合方法的解决方案.首先定义了基于对象Petri网的指控能力包服务描述和组合模型,对服务组合的数学算子进行了分析和证明,而后给出了基于OPN的服务动态组合流程,最后使用国防科大对象Petri网建模仿真环境工具对提出的服务组合模型进行建模仿真,并对实验数据进行了分析.     

基于排队PETRI网的云系统评估模型和方法

针对云系统规模庞大、构成复杂、动态性突出、层次关联性强而难于建模评估的问题,提出一种基于排队Petri网的云系统评估模型QPNC;QPNC结合了排队论和Petri网理论特点,模型具备较强的定量评价和行为描述能力,能够很好地对复杂云系统进行有效建模和模拟;基于上述模型,进一步提出并完善云系统的定量分析、评估体系,仿真并模拟了大规模并行环境下云系统的动态服务效果;实验结果表明,QPNC能够有效反映出各种云系统架构在性能和服务等方面特征,对云系统的各种动态服务行为具有很高的仿真度,为设计构建更加高效、更具针对性的云系统提供了定量分析支持和理论依据。     

物联网智能物流系统容错服务组合建模与分析

针对物流领域的服务组合存在容错性差和服务不可靠等问题，提出一种基于π网的物联网智能物流系统物流服务容错组合模型。首先，在简单介绍物联网智能物流系统后，给出了物联网智能物流系统的容错服务组合框架；然后，基于π网建立了物联网智能物流系统物流服务容错组合模型，并对模型进行了容错正确性和拟合性分析；最后，对提出的模型进行了服务可靠性、服务故障容错可靠性实验，并与Petri网、QoS动态预测算法、模糊卡诺模型和改进粒子群优化的服务组合方法针对服务组合的执行时间、用户满意度、可靠性和最优度进行对比实验。实验结果表明，所提模型具有更高的服务可靠性和服务故障容错可靠性，同时在服务组合的执行时间、用户满意度、可靠性和最优度等方面也具有一定的优越性。     

基于模板和上下文的语义Web服务动态组合

为了适应环境和上下文信息的动态变化并提供适时适地的服务,Web服务组合要能满足处于动态变化环境中的个性化用户需求。文中提出了一个基于模板和上下文的语义Web服务组合框架,该框架使用抽象服务流程进行Web服务组合建模,利用本体来进行上下文信息建模并支持基于JESS的上下文信息推理,在原有基于语义的Web服务匹配的基础上,实时地感知上下文信息来进行Web服务动态绑定。该方法提高了服务组合的成功率和动态适应性,并且满足了用户的个性化需求。     

基于业务流程的制造云服务组合模型

为了提高云制造系统中制造云服务的组合成功率,实现组合云服务与用户业务需求的准确匹配,在对制造云服务、流程节点任务、云服务的可组合性和流程匹配进行形式化描述的基础上,提出一种基于业务流程的制造云服务组合模型。该模型由业务流程引擎、业务流程、选择逻辑、评估逻辑、监控逻辑、知识库和原子云服务集构成,在功能匹配的基础上,对候选服务的可组合性进行检查,结合负载、服务质量(QoS)和业务流程信息,选择合适的云服务,并将其挂接在业务流程上实现制造云服务的组合。对制造云服务的组合流程进行了详细描述,并给出云服务组合的实现方法。实例分析表明,该模型能够有效地选择满足业务需求的云服务实体并进行组合,从而提高制造云服务的组合成功率,保障用户制造活动的顺利进行。     

基于业务预测的云资源动态调度人工智能 方法及系统研究

With the continuous development of the payment market, the data structure characteristics of new business forms such as mobile Internet have changed significantly, and intelligent cloud data center is the general trend of development in the current indus- try. This paper proposes an artificial intelligence method and system design for dynamic scheduling of cloud resources based on busi- ness prediction. The resource availability of daily physical machines has changed over time, and it is necessary to reintegrate the re- sources in order to save energy and meet the requirements of service. In the early stage of large-scale marketing, capacity analysis is combined to make prediction in advance, and intelligent multi-dimensional capacity decision expansion based on artificial intelli- gence and machine self-learning is adopted. The dynamic migration and integration method of virtual machines in cloud data centers with high energy efficiency provides a new solution for improving energy efficiency of cloud computing data centers, ensuring sys- tem reliability and reducing operation and maintenance costs of cloud data centers.     

G-TOPSIS: a cloud service selection framework using Gaussian TOPSIS for rank reversal problem

The dynamic resource requirement of applications has forced a large number of business organizations to join the cloud market and provide cloud services. It has posed a challenge for cloud users to select the best service providers and to minimize losses occurring due to its improper selection. This paper aims to propose a robust rank reversal technique for order of preference by similarity to ideal solution (TOPSIS) method based on Gaussian distribution and used to develop a cloud service selection framework. The proposed framework ranks cloud services based on the quality of services provided by cloud service providers and cloud user’s priority. A case study is performed on a real dataset obtained from CloudHarmony to show the effectiveness and correctness of the proposed framework. The results obtained demonstrate that the proposed framework ranks cloud services similar to TOPSIS-based frameworks. A sensitivity analysis has also been performed to check its robustness in six different cases causing rank reversal and found that the proposed framework is robust to handle rank reversal phenomenon in all the scenarios in comparison with other studies available in the literature.

     

A new paradigm of cloud-based predictive maintenance for intelligent manufacturing

Advances in cloud computing reshape the manufacturing industry into dynamically scalable, on-demand service oriented, and highly distributed cost-efficient business model. However it also poses challenges such as reliability, availability, adaptability, and safety on machines and processes across spatial boundaries. To address these challenges, this paper investigates a cloud-based paradigm of predictive maintenance based on mobile agent to enable timely information acquisition, sharing and utilization for improved accuracy and reliability in fault diagnosis, remaining service life prediction, and maintenance scheduling. In the new paradigm, a low-cost cloud sensing and computing node is firstly developed with embedded Linux operating system, mobile agent middleware, and open source numerical libraries. Information sharing and interaction is achieved by mobile agent to distribute the analysis algorithms to cloud sensing and computing node to locally process data and share analysis results. Comparing to the commonly used client–server paradigm, the mobile agent approach enhances the system flexibility and adaptability, reduces raw data transmission, and instantaneously responds to dynamic changes of operations and tasks. Finally, the presented cloud-based paradigm of predictive maintenance is validated on a motor tested system.     

基于概率语言术语集的云服务选择

为了解决云服务评估决策中QoS(Quality of Service,服务质量)属性的动态性刻画不足以及传统决策方法中用户主观因素过强的缺点,提出了一种基于概率语言术语集（Probabilistic Linguistic Term Set,PLTS）的选择方法。通过相似性权重与可靠性权重结合获取推荐权重,加入决策矩阵中得到综合评估矩阵;通过层次分析法（Analytic Hierarchy Process,AHP）获取的属性权重与综合评估矩阵结合得到加权综合评估矩阵;并采用TOPSIS(Technique for Order Preference by Similarity to Ideal Solution,逼近理想解排序法)方法综合评估候选服务的性能。案例分析和对比分析表明,该模型能够有效提高云服务选择的准确率与执行效率,并为云环境下的多属性决策领域提供了新的思路。     

云计算环境下的可修分布式系统可靠性分析方法

随着云计算技术的进一步发展,越来越多的应用系统托管在云计算平台上,这就对构成云计算平台的众多分布式系统的可靠性提出了更高的要求。传统分析方法难以在系统规模较大时对可修分布式系统做可靠性分析。为了提高服务质量以及降低因违反服务水平协议而导致的经济损失,本文基于马尔可夫模型提出一种适用于可修分布式系统的可靠性分析方法。通过简化系统的状态空间,在系统运行期间对其软硬件状态进行采样,并通过对分布式系统的失效过程和修复过程进行分析,根据给定时间内的失效概率序列、修复概率序列计算分布式系统的节点状态转移矩阵,得出该马尔可夫矩阵对应的稳态向量。根据特定分布式系统的自身特性,对该稳态向量进一步分析,得出系统最终的可靠性衡量指标。最后通过实验验证了该方法的可用性和有效性。     

基于柔性思想装配云服务的研究

为了满足租户的业务定制需求,云服务系统必须对租户不断变更的需求及业务领域内流程变化具有一定的适应性,为此,提出一种在柔性SaaS模式下构建云服务系统的方法。该方法以柔性思想为指导,通过服务规划,构建服务扩展结构等一系列步骤,分析设计整体服务及其扩展功能结构;经由基于插件的软件开发方法和动态组装框架Equinox,将服务及插件动态装配为“完整服务”供租户使用。把该柔性方法引入到一个物流项目中,以证实其可操作性及有效性。     

Research on dynamic business composition based on web service proxies

Web services-based business composition brings a number of advantages to the enterprise application development. How to select and compose the web services based on their functionality and QoS (Quality of Service) dynamically prove to be more and more important. In this paper we develop a proxy-based framework to compose Web services dynamically. The framework is featured with a QoS model, an effective service discovery and selection algorithms to facilitate the dynamic integration of Web services and management of abnormalities. Furthermore, a business process constructing method based on service slice is put forward to satisfy the users’ personalized requirements more effectively and flexibly. Our study concerns both functionality and QoS characteristics of Web services to identify the optimal business process solutions. A Complete case study is also included in this paper and the performance demonstrated that the framework and algorithms can provide a tangible and reliable solution to dynamic Web service composition and adaptation.     

Achieving dynamic capabilities with cloud computing: an empirical investigation

Cloud computing enables convenient and on-demand access to a shared pool of configurable computing resources. While cloud computing‘s ability to improve operational efficiency has gained much attention in the literature, there has been limited research on how it can help organizations achieve dynamic capabilities. Drawing from dynamic capabilities theory, we conducted a field study using a multiple case study design to examine the following research question: How do organizations achieve dynamic capabilities by using Cloud Computing? We develop a framework that explains how organizations respond to market dynamism by developing sense-and-response strategies that enable them to achieve dynamic capabilities using business process redesign, business network redesign, and business scope redefinition. We discuss how these transformations, in turn, improve organizational outcomes such as service effectiveness and efficiency. Our study also identifies factors that support and hinder the development of dynamic capabilities. Our study contributes to the literature on dynamic capabilities by examining how IT capabilities like cloud computing may accelerate the ability of an organization to achieve dynamic capabilities. We also identify transformational changes of business processes and inter-organizational networks that are enabled by cloud computing. Further, we identify how the essential characteristics of cloud computing support sense and respond strategies.     

利用优先级双重深度强化学习的自适应Web服务组合方法

针对大规模Web服务组合在动态环境下难以实现高可靠性、高动态适应能力的问题,提出一种结合优先级双重强化学习和POMDP的自适应Web服务组合方法;首先,采用POMDP对大规模Web服务组合优化策略进行建模,简化了组合优化分析的步骤,提高了大规模Web组合服务的效率;然后,在POMDP基础上,利用双重深度强化学习方法对优化策略进行分层重构,并求取最优解,提高了组合服务对动态服务环境的适应能力;实验结果表明,与现有优秀方法相比,所提方法在可靠性、效率和动态环境适应能力方面均有显著提升。