Multi-group QoS consensus for web services

QoS has been considered as a significant factor for web service marketing and selection. The interpretation of QoS value from web service consumers and providers would be very different. However, a large group of web service participants with different backgrounds may have difficulties in reaching consensus on the values of multi-dimensional web service QoS, so they may have to be clustered in multi-groups in order to improve effectiveness and efficiency. The similarity of clustered fuzzy QoS dispositions as well as their preference order over these attributes should be analyzed to form a multi-groups consensus framework. A soft multi-groups clustering approach could be adopted to prevent opinions from being excluded unintentionally. The group boundaries and similarity thresholds which are used for clustering and analyzing fuzzy QoS opinions can be moderated dynamically according to the feedback from the internal learning mechanism and the web service consumers. As a result, a model for marketing web services based on multi-group consumers' QoS consensus, the FMG-QCMA (Fuzzy Multi-Groups based QoS Consensus Moderation Approach), is proposed to meet the above requirements. The proposed FMG-QCMA is also evaluated through a case study to demonstrate its effectiveness and efficiency in relation to an existing framework, QCMA (QoS Consensus Moderation Approach).     

Probabilistic approach for QoS-aware recommender system for trustworthy web service selection

We present a QoS-aware recommender approach based on probabilistic models to assist the selection of web services in open, distributed, and service-oriented environments. This approach allows consumers to maintain a trust model for each service provider they interact with, leading to the prediction of the most trustworthy service a consumer can interact with among a plethora of similar services. In this paper, we associate the trust in a service to its performance denoted by QoS ratings instigated by the amalgamation of various QoS metrics. Since the quality of a service is contingent, which renders its trustworthiness uncertain, we adopt a probabilistic approach for the prediction of the quality of a service based on the evaluation of past experiences (ratings) of each of its consumers. We represent the QoS ratings of services using different statistical distributions, namely multinomial Dirichlet, multinomial generalized Dirichlet, and multinomial Beta-Liouville. We leverage various machine learning techniques to compute the probabilities of each web service to belong to different quality classes. For instance, we use the Bayesian inference method to estimate the parameters of the aforementioned distributions, which presents a multidimensional probabilistic embodiment of the quality of the corresponding web services. We also employ a Bayesian network classifier with a Beta-Liouville prior to enable the classification of the QoS of composite services given the QoS of its constituents. We extend our approach to function in an online setting using the Voting EM algorithm that enables the estimation of the probabilities of the QoS after each interaction with a web service. Our experimental results demonstrate the effectiveness of the proposed approaches in modeling, classifying and incrementally learning the QoS ratings.     

On optimal decision for QoS-aware composite service selection

The increasing popularity of employing web services for distributed systems contributes to the significance of service discovery. However, duplicated and similar functional features existing among services require service consumers to include additional aspects to evaluate the services. Generally, the service consumers would have different view on the quality of service (QoS) of service attributes. How to select the best composite service in theory among available service (WS) candidates for consumers is an interesting practical issue. This work proposes a QoS-aware service selection model based on fuzzy linear programming (FLP) technologies, in order to identify their dissimilarity on service alternatives, assist service consumers in selecting most suitable services with consideration of their expectations and preferences. This approach can obtain the optimal solution of consensual weight of QoS attribute and fuzzy positive ideal solution (FPIS) by extending LINMAP method, developed by Srinivasan and Shocker. Finally, two numerical examples are given to demonstrate the process of QoS-aware web service selection. The experimental results demonstrated that it is a feasible and supplementary manner in selecting the of web services.     

Reaching consensus: A moderated fuzzy web services discovery method

Web services are used for developing and integrating highly distributed and heterogeneous systems in different domains such as e-business, grid services, and e-government systems. Web services discovery is a key to dynamically locating desired web services across the Internet. Prevailing research trend is to dynamically discover and compose web services in order to develop composite services that provide enhanced functionality. Existing discovery techniques do not take into account the diverse preferences and expectations of service consumers and providers which are generally used for searching or advertising web services. This paper presents a moderated fuzzy web service discovery approach to model subjective and fuzzy opinions, and to assist service consumers and providers in reaching a consensus. The method achieves a common consensus on the distinct opinions and expectations of service consumers and providers. This process is iterative such that further fuzzy opinions and preferences can be added to improve the precision of web service discovery. The proposed method is implemented as a prototype system and is tested through various experiments. Experimental results demonstrate the effectiveness of the proposed method.     

An evidence-based scheme for web service selection

Existing studies on the web service selection problem focus mainly on the functional QoS properties of the service rather than the consumer satisfaction and trust aspects. While a good QoS enhances the reputation of a service, different consumers invariably hold differing views of the service contents. Some service reputation approaches primarily consider the consumer’s prior experience of the service via opinion feedback system, may neglect the effect of social trust transition in the recommendations of others. As a result, the problem of reaching consensus on the level of consumer trust regarding service becomes one of key issues in service selection. This study proposes a trust-based service selection model to estimate the degree of consumer trust in a particular service based on the consumers’ direct experience and indirect recommendation of the service. In the proposed approach, the degree of consumer trust is correctly estimated by extending Dempster–Shafer evidence reasoning theory to the reputation computation using consumers’ direct experience and incorporating Jøsang’s belief model for solving the trust transition problem in the indirect recommendation of the service. The proposed model effectively enables deception detection by means of existing bodies of evidence, and therefore excludes the fraudulent evidence of malicious evaluators from the selection process. In addition, a quality index is proposed to help third party (TTP) examine the body of evidence and make the outranking result more reliable. Importantly, the quality index is based not only on the confidence degree of the evidence, but also on the support degree, and therefore discovers the effects of intentional negative assessments. The validity of the proposed approach is demonstrated numerically by means of two service selection examples.     

一种基于组合赋权法的Web服务选择策略*

针对Web服务选择中服务请求偏好权重表达的模糊性及服务质量(QoS)属性值间存在的相互制约关系等问题,提出一种基于组合赋权法的Web服务选择策略。首先利用模糊层次分析法(FAHP)和主成分分析法(PCA)分别计算主观QoS权重和客观QoS权重;然后综合主、客观QoS权重利用组合赋权法(CWA)计算服务请求的综合QoS权重;最后提出一种综合评价函数,以保障所选择的服务在满足服务请求偏好的基础上,能更准确地反映候选Web服务总体QoS水平。实例分析表明该方法是有效的。     

A user centric service-oriented modeling approach

With rapid development of service-oriented architecture and cloud computing, web services have been widely employed on the Internet. Quality of Service (QoS) is a very important criterion for service consumers to measure and select services. The selection of web services with respect to non-functional QoS criteria can be considered as a Multiple Criteria Decision Making (MCDM) problem when multiple consumers need to share a number of services. This paper describes a new user centric service-oriented modeling approach which is featured by integrating fuzzy Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and Service Component Architecture (SCA) to facilitate web service selection and composition and to effectively satisfy a group of service consumers’ subjective requirements and preferences in the dynamic environment. The main contribution of this method is able to translate a group of users’ fuzzy requirements to services as well as model different levels of hardware and software as services to meet the requirements. We also design a simulated environment that includes 8*8 LED matrix on a circuit board that corresponds to an office with different appliances to demonstrate the dynamic service selection and binding. The simulation is used to assess the computational efficiency of the fuzzy TOPSIS method and the effectiveness of the proposed system.     

基于反馈QoS量化的Web服务发现模型

服务发现是Web服务应用中极其重要的环节.随着Web服务数目的增长,提供相同功能的服务越来越多,现有的基于服务功能描述的服务发现机制越来越难以满足实际需求,于是向Web服务描述内部加入非功能性描述的成为Web服务应用的一个新研究方向.提出了一种基于反馈信息的QoS量化的Web服务发现和选择模型,采用了反馈机制对注册服务QoS进行量化,通过引入概念相近权重,实现了服务质量动态排序,满足了用户的个性化需求,提高服务的查准率.     

一种支持QoS约束的Web服务发现模型

服务质量(QoS)描述了一个产品或服务满足消费者需求的能力．提供有保证的QoS是Web服务在商业应用中获得成功的关键．如何动态地选择出最适合用户需求的Web服务正在引起研究领域的关注．该文提出了一种支持QoS约束的Web服务发现模型(WSDM-Q)．该模型定义了一组描述Web服务QoS及信誉度的分类tModel,引入了QoS量化的概念,采用了QoS协商和反馈机制,支持携带QoS描述信息的服务发布和基于QoS约束的服务发现．协商机制允许服务提供者根据运行时的状态对服务请求进行接纳控制,反馈机制允许服务注册中心对服务提供QoS的信誉度进行动态评估和调整。该模型不需要改变UDDI的内部实现,能够直接在现有的UDDI服务上进行扩展．作者已经基于Mierosoft.Netf平台和Microsoft UDDI服务实现了一个原型系统．     

动态Web服务合成中的服务选择算法研究

为了提高服务合成效率并更好的适应动态变化的应用环境,提出了基于流程修改的服务合成方案,使得复合服务的执行与服务发现过程并行化。提出了基于QoS属性的服务选择算法,该算法能够实现服务执行时选择服务,在考虑综合QoS信息及时间因素的基础上,通过不断更新用户需求来进行服务选择,提高了服务合成效率。实验结果表明,该选择算法可以获得满足用户需求且最优的服务,很好的保证了服务的可靠执行。     

An efficient algorithm for partially matched services in internet of services

Internet of Things (IoT) connects billions of devices in an Internet-like structure. Each device encapsulated as a real-world service which provides functionality and exchanges information with other devices. This large-scale information exchange results in new interactions between things and people. Unlike traditional web services, internet of services is highly dynamic and continuously changing due to constant degrade, vanish and possibly reappear of the devices, this opens a new challenge in the process of resource discovery and selection. In response to increasing numbers of services in the discovery and selection process, there is a corresponding increase in number of service consumers and consequent diversity of quality of service (QoS) available. Increase in both sides’ leads to the diversity in the demand and supply of services, which would result in the partial match of the requirements and offers. This paper proposed an IoT service ranking and selection algorithm by considering multiple QoS requirements and allowing partially matched services to be counted as a candidate for the selection process. One of the applications of IoT sensory data that attracts many researchers is transportation especially emergency and accident services which is used as a case study in this paper. Experimental results from real-world services showed that the proposed method achieved significant improvement in the accuracy and performance in the selection process.     

基于可信QoS的路径查找及负载均衡策略研究

Web服务是云计算中资源调用的有效方式。单一Web服务功能往往有限,只能完成特定任务。服务组合则可以将多种Web服务形成有效的调用序列,实现更为强大的功能。服务发布量以及服务请求量的迅速激增带来了新的安全问题。首先,现有的服务组合方案均以服务质量(QoS)为依据进行Web服务选择,但服务质量通常由服务发布者提供,存在服务发布者发布虚假QoS值诱骗用户的欺诈现象;其次,传统的服务组合方案只生成一条最优路径,当恶意请求持续访问时,会造成某服务节点瘫痪,甚至整个服务组合系统失效。因此,针对服务质量恶意欺诈的问题,文章提出一种可信的QoS计算模型,根据Web服务发布者的信用综合评估服务质量;针对单一最优路径无法满足大量请求的问题,文章提出一种路径发现和负载均衡的多路径方法。仿真结果表明,文章提出的方法不仅能提高服务组合的成功率,满足用户的需求,而且能找到更多的服务组合方案执行。     

Functionality Aware Dynamic Composition of Web Services

The composition of the web service is a common technique to attain the best results of complex web tasks. The selection of appropriate web services, linking those services in the action flow and attaining the actual functionality of the task are the important factors to be considered. Even though different frameworks and methods have been proposed to dynamically compose web services, each method has its advantage and disadvantage over the other. Most of the methods give much importance to the Quality of Service (QoS) but fail to achieve the actual functionality after composition. This paper proposes a functionality-oriented composition technique for composing web services. Moreover, this method helps reach the extreme functionality of each web service in the composition towards customer satisfaction. Apart from considering the overall QoS of every single service, the non-functional parameters associated with these services are also considered for achieving the expected functionality. Each of these non-functional parameters has a vital role in the functional performance of the web service. The web services that satisfy the non-functional requirements are chosen to form the composition to attain the best performance. The list of services in the proposed composition method is different from the conventional one, which is composed based on the overall QoS. The non-functional parametric values, the QoS of each web service and the overall QoS after composition are evaluated for the proposed method and experimentally analyzed to prove their advantage over the others.     

A trustworthy QoS-based collaborative filtering approach for web service discovery

Many network services which process a large quantity of data and knowledge are available in the distributed network environment, and provide applications to users based on Service-Oriented Architecture (SOA) and Web services technology. Therefore, a useful web service discovery approach for data and knowledge discovery process in the complex network environment is a very significant issue. Using the traditional keyword-based search method, users find it difficult to choose the best web services from those with similar functionalities. In addition, in an untrustworthy real world environment, the QoS-based service discovery approach cannot verify the correctness of the web services’ Quality of Service (QoS) values, since such values guaranteed by a service provider are different from the real ones. This work proposes a trustworthy two-phase web service discovery mechanism based on QoS and collaborative filtering, which discovers and recommends the needed web services effectively for users in the distributed environment, and also solves the problem of services with incorrect QoS information. In the experiment, the theoretical analysis and simulation experiment results show that the proposed method can accurately recommend the needed services to users, and improve the recommendation quality.     

一种考虑QoS数据可信性的服务选择方法

随着Internet上功能相似的Web服务的逐渐增多,在运行时刻基于服务质量(QoS)对Web服务进行查找和选择已成为研究热点.现有的基于QoS的服务选择方法通常假定服务提供者和使用者给出的QoS数据都是真实可信的,然而这一假设在实际中往往很难保证.为此,提出了一种考虑QoS数据可信性的服务选择方法.方法从QoS数据来源的角度对质量属性进行分类和计算:对于数据来自服务提供者的质量属性,使用以往运行数据统计,对提供者的QoS数据进行修正;对于数据来自服务使用者的质量属性,通过计算用户间以往反馈的相似程度权衡不同QoS反馈数据的可信程度.对此给出了实现框架,并通过一组模拟实验说明该方法能够有效地削弱不可信的QoS数据对服务选择的影响,增强了Web服务选择结果的准确性.     

Globally optimal selection of web composite services based on univariate marginal distribution algorithm

Quality of service (QoS) model of composite services and web service selection based on QoS are currently the hot issues in the web service composition area. Service selection based on QoS, which is a globally optimal selection issue, is a NP-hard problem. Taking engine into consideration, this paper develops a QoS model for service selection in the web composite services. We use the algorithm on the estimation of distribution to solve the NP-hard problem of service selection and present a web service selection method based on the univariate marginal distribution algorithm (UMDA). Simulation analysis and experimental study based on the UMDA method are carried out. It is proved that the method is effective in solving the NP-hard problem.     

基于QoS的Web服务动态组合模型

提出一种基于QoS的Web服务动态组合模型,可以根据用户对服务质量的请求,动态地选择最适合的Web服务进行绑定.与原有模型相比,扩展了服务代理的功能.其提供的绑定功能可用于保存服务调用的上下文和历史信息,以便提高下次访问的效率.另外,支持对子服务的划分,可以将大粒度的服务请求划分成子服务,并对服务的调用次序进行更合理地安排和优化.性能分析结果表明,该模型极大地提高了效率.     

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.