A framework for QoS computation in web service and technology selection

There is a global trend of convergence of voice, video and data. Industry is facing a major shift from circuit-based voice networks to packet-based converged networks. In the last few years, data networks have been growing at much faster rate than voice networks, mainly due to the growth of Internet. As a result of this trend, more and more data are being sent over Data Networks (Voice over IP, Voice over Frame Relay, and Voice over ATM). The standards used are SOAD, WSDL, and UDDI. The emerging Service-Oriented Computing (SOC) paradigm promises to enable businesses and organizations to collaborate in an unprecedented way by means of standard web services. To support rapid and dynamic composition of services in this paradigm, web services that meet requesters' functional requirements must be able to be located from a large and constantly changing number of service providers based on their Quality of Service (QoS). In order to enable quality-driven web service selection, we need an open, secure and fair framework to evaluate the QoS of a vast number of web services. The fair computation and enforcing of web services should have minimal overhead but yet able to achieve sufficient trust by both service requesters and providers. In this paper I present our open and fair QoS computation model for web service selection.     

Context-aware QoS prediction for web service recommendation and selection

QoS prediction is one of the key problems in Web service recommendation and selection. The context information is a dominant factor affecting QoS, but is ignored by most of existing works. In this paper, we employ the context information, from both the user side and service side, to achieve superior QoS prediction accuracy. We propose two novel prediction models, which are capable of using the context information of users and services respectively. In the user side, we use the geographical information as the user context, and identify similar neighbors for each user based on the similarity of their context. We study the mapping relationship between the similarity value and the geographical distance. In the service side, we use the affiliation information as the service context, including the company affiliation and country affiliation. In the two models, the prediction value is learned by the QoS records of a user (or a service) and the neighbors. Also, we propose an ensemble model to combine the results of the two models. We conduct comprehensive experiments in two real-world datasets, and the experimental results demonstrate the effectiveness of our models.     

动态选择Web服务的QoS本体设计

QoS信息是动态选择Web服务的重要依据.本体描述可以使Web服务成为机器可读、可理解和可操作的实体,从而可实现Web服务的自动组合.分析了Web服务的各种Qos参数,设计了一个分层的OWL描述QoS本体结构,分别表示Web服务不同方面的QoS信息,并根据Agent的思想给出了一个Web服务的改进结构,这样Web服务的使用者可利用它们动态选择最合适的Web服务.     

一种基于QoS的Web服务发现模型

当前采用 OWL-S 描述的 Web 服务,主要从服务功能进行描述,缺乏对服务质量的明确刻画,导致服务请求者难以得到最佳服务.针对此问题,本文对 OWL-S 规范扩展,提出一个基于 QoS 的语义 Web 服务发现模型 SWSDM-QoS.该模型支持 QoS 的服务发布、查找和反馈,并在此基础上提出一种 QoS 约束的服务选择算法.本论文创新性之处在于在服务发现过程中加入了服务质量本体信息,使得Web服务在框架内支持QoS约束的语义发现,满足了用户的个性化需求.     

基于QoS的Web服务选择方法研究新进展*

随着服务计算、云计算技术的迅速发展,具有相同或相似功能但服务质量（QoS）不同的可用服务已大规模部署在互联网上。如何从这些海量Web服务中为服务组合选择令人满意的服务成了一个重要的研究问题,其选择方法是影响组合服务的QoS 和服务组合性能高低的关键因素。为了解决该问题,研究者们已开发了许多服务选择方法以帮助用户选择合适的服务。针对近五年基于QoS的Web服务选择方法的新进展进行了综述。归纳了包括运用Skyline计算、协同过滤、优化算法、多属性决策支持理论和混合方法等一些典型的服务选择方法,讨论了各方法的优势和局限性,并根据当前研究现状,探讨了该领域的发展趋势和未来研究方向和挑战。     

A novel feature selection framework for automatic web page classification

The number of Internet users and the number of web pages being added to WWW increase dramatically every day.It is therefore required to automatically and e?ciently classify web pages into web directories.This helps the search engines to provide users with relevant and quick retrieval results.As web pages are represented by thousands of features,feature selection helps the web page classifiers to resolve this large scale dimensionality problem.This paper proposes a new feature selection method using Ward s minimum variance measure.This measure is first used to identify clusters of redundant features in a web page.In each cluster,the best representative features are retained and the others are eliminated.Removing such redundant features helps in minimizing the resource utilization during classification.The proposed method of feature selection is compared with other common feature selection methods.Experiments done on a benchmark data set,namely WebKB show that the proposed method performs better than most of the other feature selection methods in terms of reducing the number of features and the classifier modeling time.     

一种基于QoS约束的Web服务选择模型

针对Web服务质量(QoS)评价结果准确性较低的问题,提出了一种新的基于QoS约束的语义Web服务选择和排序模型,引入诱导有序加权平均算子(induced ordered weighted averaging operator,IOWA),充分利用服务的历史QoS信息来对各QoS属性值进行预测,并依据属性的信誉值分配发...     

A service accountability framework for QoS service management and engineering

Service science, management and engineering (SSME) research is to study the methodology and technology for service innovation, design, development and delivery. Since service industry is very quality-sensitive and trust-dependent, we propose a service accountability management framework to detect, diagnose, defuse and disclose the root cause for any problematic service process. The accountability support is important for SSME since service processes often rely on external service providers to deliver part of the service functionalities. A service system must have effective yet efficient mechanisms to ensure that every external service is delivering a consistent and acceptable level of performance to meet the end-to-end quality of service (QoS) of the whole service process. In this paper, we present the accountability framework, identify the components in an accountable service architecture, and design an accountability diagnosis methodology. We also briefly present the inteLLigent Accountability Management Architecture (LLAMA) project which implements the accountability service bus (ASB), an agent-based middleware to support the monitoring, diagnosis, and reconfiguration of e-services. LLAMA ASB interacts with accountability agents to monitor services and the Accountability Authority to automatically diagnose faulty situations. The LLAMA technology is useful to ensure the QoS in SSME-based systems.     

A graph-based QoS prediction approach for web service recommendation

Applied Intelligence - With the development of the Internet, the recommendation based on Quality of Service(QoS) is proven to be an efficient way to deal with the ever-increasing web services in...     

一种策略驱动的Web服务质量保障框架

随着面向服务计算技术的发展和应用,提供非功能属性保障能力成为服务在企业应用中获得成功的关键因素.同传统的分布式计算技术不同的是,服务是动态发现和绑定的,这要求服务双方必须动态协商并保障各种非功能属性.使用WS-Policy语言来显式描述Web服务的非功能性的能力和需求,并提出了一种策略驱动的服务保障框架来完成非功能属性的动态保障,最后在SOAP引擎SOAPExpress中实现了这一框架,结果表明它可以有效满足企业应用对Web服务质量的需求.     

基于Web 服务和工作流的MIS框架模型的研究

分析了目前企业管理信息系统的不足,介绍了Web服务和工作流技术,提出了一种基于Web服务和工作流技术的企业管理信息系统框架,给出了系统的体系结构和软件框架模型,为建立企业管理信息系统提供了新的思路。最后给出了一具体实例,介绍了该模型的应用。     

扩展Augment UDDI系统以支持QoS的Web服务发现模型

服务质量表达了一个服务满足使用者需求的能力,在Web服务发现过程中起重要作用.针对现有服务发现系统对服务质量支持不足的缺点,在深入研究现有Web服务发现模型的基础上,对Augment UDDI注册系统框架做了针对QoS的扩展,提出一个支持QoS的语义Web服务发现模型SWSDM-QoS.在模型中,设计了一个分阶段匹配的语义Web服务发现策略,给出了每个阶段的具体匹配算法.并引入相似度函数对Augment UDDI注册系统所采用的等级匹配算法进行了改进,弥补了匹配结果过于粗糙的缺点.最后设计了一组实验,对比分析了针对QoS扩展的可行性和有效性.     

一种支持QoS的语义Web服务发现框架研究

随着Web服务的广泛使用,Web服务的发现机制显得尤为重要.现有的Web服务发现方法,因缺乏对服务质量(QoS)的描述而难以为用户选择最佳服务.主要提出一种考虑QoS的语义Web服务发现框架,采用分类和反馈机制,及相应的匹配级别和过滤机制,以便用户查找到与自己需求最接近的服务,提高服务的查找率.     

一种QoS与语义关联驱动的服务选取方法

在基于服务的应用中,增值的Web服务可以通过在候选服务中选取一些服务（即服务组件）组合组成。通过分析组合服务的特性,将QoS和服务组件间的语义关联度作为组合服务选取的重要指标,提出了组合服务的语义关联模型,模型综合考虑了服务间的数据逻辑关系以及条件依赖关系对于组合服务的影响,基于该模型将服务选取问题形式化为多目标优化问题,采用遗传算法（Genetic Algorithm（GA））求解。实验结果表明该方法是可行而有效的。     

A novel ANN-based service selection model for ubiquitous computing environments

How to choose an appropriate service from all the usable services regardless of user's location and heterogeneous architecture of underlying software and hardware infrastructure is the most important study content in ubiquitous computing domain. In order to overcome the shortcomings of blindness and randomicity in traditional and improved trust-mechanism-based service selection models, we propose a novel ANN-based (Artificial Neural Network) service selection model (called the ANNSS model). We adopt a novel method which according to the earlier information of the cooperation between the devices and the context information, an ANN-based evaluation standard for the service quality of service provider is given out so that user can acquire an effective guidance and choose the most appropriate service. At the same time, we improved the traditional BP algorithm based on three-term method (called the TTMBP algorithm) consisting of a learning rate (LR), a momentum factor (MF) and a proportional factor (PF) in order to satisfy the requirements of time issue in real-time system. The convergence speed and stability were enhanced by adding the proportional factor. The self-adjusting architecture method is adopted so that a moderate scale of neural network can be obtained. We have implemented the ANNSS algorithm in an actual power supply system for communication devices and fulfilled various simulations. The results of simulation show that the proposed service selection scheme is not only scalable but also efficient, and that the novel BP algorithm based on three-term has high convergence speed and good convergence stability. The novel service selection scheme superior to the traditional and improved trust-mechanism-based service selection scheme. The novel scheme can exactly choose a most appropriate service from many service providers and provide the most perfect service performance to users.     

A structural-semantic web service selection approach to improve retrievability of web services

Service-Oriented Computing promotes building applications by consuming and reusing Web Services. However, the selection of adequate Web Services given a client application is still a major challenge. The effort of assessing and adapting candidate services could be overwhelming due to the “impedance” of Web Service interfaces expected by clients versus the actual interfaces of retrieved Web Services. In this work, we present a novel structural-semantic approach to help developers in the retrieval and selection of services from a service registry. The approach is based on a comprehensive structural scheme for service Interface Compatibility analysis, and WordNet as the semantic support to assess identifiers of operations and parameters. We also empirically analyze, compare and contrast the performance of three service selection methods: a pure structural approach, a pure semantic approach, and the structural-semantic (hybrid) approach proposed in this work. The experimental analysis was performed with two data-sets of real-world Web Services and a service discovery support already published in the literature. Results show that our hybrid service selection approach improved effectiveness in terms of retrievability of Web Services compared to the other approaches.     

QoS-aware web service selection with negative selection algorithm

Web service selection, as an important part of web service composition, has direct influence on the quality of composite service. Many works have been carried out to find the efficient algorithms for quality of service (QoS)-aware service selection problem in recent years. In this paper, a negative selection immune algorithm (NSA) is proposed, and as far as we know, this is the first time that NSA is introduced into web service selection problem. Domain terms and operations of NSA are firstly redefined in this paper aiming at QoS-aware service selection problem. NSA is then constructed to demonstrate how to use negative selection principle to solve this question. Thirdly, an inconsistent analysis between local exploitation and global planning is presented, through which a local alteration of a composite service scheme can transfer to the global exploration correctly. It is a general adjusting method and independent to algorithms. Finally, extensive experimental results illustrate that NSA, especially for NSA with consistency weights adjusting strategy (NSA+), significantly outperforms particle swarm optimization and clonal selection algorithm for QoS-aware service selection problem. The superiority of NSA+ over others is more and more evident with the increase of component tasks and related candidate services.