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。     

基于位置聚类和张量分解的Web服务推荐算法

基于服务质量（QoS）的Web服务推荐能在众多功能相似的Web服务中发现最能满足用户非功能需求的Web服务,但QoS属性值预测算法仍存在预测准确度不高和数据稀疏性的问题。针对以上问题,提出了一种基于位置聚类和分层张量分解的QoS预测算法ClustTD,该算法基于用户和服务的位置属性将用户和服务聚类成多个局部组,分别对局部组和全局的用户、服务和时间上下文进行张量建模和分解,将局部和全局张量分解的QoS预测值进行加权组合,同时考虑了局部和全局因素,获得最终QoS预测值。实验结果表明,该算法具有较高的QoS预测准确率和Web服务推荐质量,并能在一定程度上解决数据稀疏性问题。     

基于二维QoS模型的Web服务组合

在实际应用中,单个Web服务通常无法满足复杂应用的需求,如何组合已有的服务,从而提供更强大更完整的商业功能已成为此领域的研究热点。然而现行的SOA架构中,Web服务组合方法中很少考虑Web服务质量（QoS）问题,或者对Qos考虑得不够全面,这样,组合出的服务不能确保能够满足用户的QoS需求。针对这一问题,提出了Web服务的二维QoS模型,并使用了改进的UDDI规范,在此基础上给出了基于QoS的Web服务组合的有色Petri网组合策略。在满足用户组合服务的功能需求的同时,也满足了用户对服务质量QoS的需求,实现了需求服务的优化。     

A probabilistic approach to modeling and estimating the QoS of web-services-based workflows

Web services promise to become a key enabling technology for B2B e-commerce. One of the most-touted features of Web services is their capability to recursively construct a Web service as a workflow of other existing Web services. The quality of service (QoS) of Web-services-based workflows may be an essential determinant when selecting constituent Web services and determining the service-level agreement with users. To make such a selection possible, it is essential to estimate the QoS of a WS workflow based on the QoSs of its constituent WSs. In the context of WS workflow, this estimation can be made by a method called QoS aggregation. While most of the existing work on QoS aggregation treats the QoS as a deterministic value, we argue that due to some uncertainty related to a WS, it is more realistic to model its QoS as a random variable, and estimate the QoS of a WS workflow probabilistically. In this paper, we identify a set of QoS metrics in the context of WS workflows, and propose a unified probabilistic model for describing QoS values of a broader spectrum of atomic and composite Web services. Emulation data are used to demonstrate the efficiency and accuracy of the proposed approach.     

Adaptive Service Composition in Flexible Processes

In advanced service oriented systems, complex applications, described as abstract business processes, can be executed by invoking a number of available Web services. End users can specify different preferences and constraints and service selection can be performed dynamically identifying the best set of services available at runtime. In this paper, we introduce a new modeling approach to the Web service selection problem that is particularly effective for large processes and when QoS constraints are severe. In the model, the Web service selection problem is formalized as a mixed integer linear programming problem, loops peeling is adopted in the optimization, and constraints posed by stateful Web services are considered. Moreover, negotiation techniques are exploited to identify a feasible solution of the problem, if one does not exist. Experimental results compare our method with other solutions proposed in the literature and demonstrate the effectiveness of our approach toward the identification of an optimal solution to the QoS constrained Web service selection problem     

一种面向多用户的负载感知动态服务选择模型

在面向多用户的动态环境中进行基于QoS 的服务选择需要面临诸多挑战,而动态的服务负载就是其中之一.当前的服务选择方法难以在多用户多业务的开放环境下应对服务执行时的负载动态变化,缺乏实时感知负载的应变能力.针对这一问题,首先,提出一种基于负载等级的服务多维QoS 模型（load level based multidimensional QoS,简称LLBMQoS）;在此基础上,提出了一种面向多用户的负载感知的动态服务选择模型（load-aware dynamic serviceselection model,简称LADSSM）以实现动态负载环境下的服务优化选择.该模型采用两阶段服务选择：在组合服务规划阶段,生成候选服务队列;在组合服务执行阶段,依据当前负载状态实现服务的动态选择;最后,仿真实验的结果表明：该模型较好地适应了多用户动态环境下的服务负载变化,能够在保证用户端到端QoS需求的前提下,及时而有效地提供效用优化的服务选择方案.     

面向用户群体的Web服务推荐

针对Web服务推荐中服务用户调用Web服务的服务质量数据稀疏性导致的低推荐质量问题,提出了一种面向用户群体并基于协同过滤的Web服务推荐算法(WRUG)。首先,为每个服务用户根据用户相似性矩阵构建其个性化的相似用户群体;其次,以相似用户群体中心点代替群体从而计算用户群体相似性矩阵;最后,构造面向群体的Web服务推荐公式并为目标用户预测缺失的Web服务质量。通过对197万条真实Web服务质量调用记录的数据集进行对比实验,与传统基于协同过滤的推荐算法(TCF)和基于用户群体影响的协同过滤推荐算法(CFBUGI)相比,WRUG的平均绝对误差下降幅度分别为28.9%和4.57%;并且WRUG的覆盖率上升幅度分别为110%和22.5%。实验结果表明,在相同实验条件下WRUG不仅能提高Web服务推荐系统的预测准确性,而且能显著地提高其有效预测服务质量的百分比。     

基于用户视角的组合Web服务响应时间优化

针对响应时间,通过选择、调用合适的成员Web服务,对组合Web服务的QoS进行优化．该优化方法基于用户的视角,将组合Web服务运行时用户一次等待周期中需要执行的所有任务作为一个整体进行调度;同时根据组合Web服务流程的不同环节中用户对等待时间的不同敏感度,设置调度参数,降低关键环节超时的风险．实验表明,此方法可以有效地优化组合Web服务的响应时间,提高用户的满意度和QoS．     

一种支持服务关联的Web服务选择方法

目前存在的大多数基于QoS(quality of service)的Web服务选择方案都忽略了服务之间的关联关系, 认为服务之间是相互独立的, 使得组合服务的结果与实际有所偏差。为了提高组合Web服务QoS的精确性, 考虑到服务之间的关联关系对组合服务质量的影响, 分别对服务之间的接口关联、业务关联和统计关联三种关联关系进行研究, 提出了一种基于关联度的关联服务集合发现方法, 将支持服务关联的选择问题转换为混合整数规划模型进行求解。实验结果表明, 考虑服务之间的关联关系会提高组合Web服务的质量。     

一种QoS感知的Web服务群体评价方法

传统QoS感知的Web服务选择方法需要用户给出精确权重以表达其对QoS属性的偏好。为解决用户无明确偏好情况下的服务选择问题,提出了一种QoS感知的Web服务群体评价方法。对Web服务群体评价的需求进行了详细的分析,以有明确QoS偏好的用户对服务的历史选择结果为基础,利用群决策的Condorcet和Borda函数集结这些结果得到服务的群体评价,从而使得无明确QoS偏好用户可以参考该评价结果进行服务选择。理论分析和仿真实验验证了该服务群体评价方法的合理性和有效性。     

一种基于服务关系与效能的Web服务选择方法

当前Web服务选择是一个研究热点,很多研究人员提出根据用户对QoS（Quality of Service）属性的偏爱,把指定属性权值计算QoS属性综合值作为服务选择依据。实际应用中,用户对服务的偏爱是主观的,在多备选服务,多QoS属性参照的条件下主观性被进一步放大,会导致所选择的服务并不是最适合的。同时,服务之间在社会层次的相互关系并未在服务选择中被考虑。针对上述两个问题,文中首先设计了Web服务选择二维度量属性模型,引入了社会域的服务作用关系;其次通过QoS约束条件排除不满足用户要求的服务;最后设计了一种基于服务效能偏爱的服务选择方法,并通过实验数据验证了该服务选择方法的可行性、有效性和客观性。     

Web Service Composition Based on QoS Rules

For workflow-based service composition approach, the relations between the Web service QoS and environments are usually not considered, so that the information about QoS for composite service selection is inaccurate. It makes the selected composite service inefficient, or even unexecutable. To address this problem, a novel service composition approach based on production QoS rules is proposed in this paper. Generally, it is very difficult to directly analyze how different kinds of environment factors influence the Web service QoS. We adopt “black-box” analysis method of optimizing composite services, discovering the knowledge such as “the QoS of one Web service will be higher in specific environments”. In our approach, the execution information of the composite service is recorded into a log first, which will be taken as the basis of the subsequent statistical analysis and data mining. Then, the timely QoS values of the Web services are estimated and the production QoS rules being used to qualitatively express the different performances of the Web service QoS in different environments are mined. At last, we employ the mined QoS knowledge of the Web services to optimize the composite service selection. Extensive experimental results show that our approach can improve the performance of selected composite services on the premise of assuring the selecting computation cost.     

QoS-aware service selection via collaborative QoS evaluation

We present in this paper a novel collaborative filtering based scheme for evaluating the QoS of large scale Web services. The proposed scheme automates the process of assessing the QoS of a priori unknown service providers and thus facilitates service users in selecting services that best match their QoS requirements. Most existing service selection approaches ignore the great diversity in the service environment and assume that different users receive identical QoS from the same service provider. This may lead to inappropriate selection decisions as the assumed QoS may deviate significantly from the one actually received by the users. The collaborative filtering based approach addresses this issue by taking the diversity into account instead of uniformly applying the same QoS value to different users. They predict a user’s QoS on an unknown service by exploiting the historical QoS experience of similar users. Nevertheless, when only limited historical QoS data is available, these approaches either fail to make any predictions or make very poor ones. The cornerstone of the proposed QoS evaluation scheme is a Relational Clustering based Model (or RCM) that effectively addresses the data scarcity issue as stated above. Experimental results on both real and synthetic datasets demonstrate that the proposed scheme can more accurately predict the QoS on unknown service providers. The efficient performance also makes it applicable to QoS evaluation for large scale Web services.     

基于灰色关联分析的Web服务选择

为方便用户选择最优Web服务,利用灰色系统理论对Web服务质量QoS属性因子进行分析,提出了一种基于用户QoS偏好的Web服务灰色关联分析方法。考虑到Web服务QoS的不确定性,该方法使用区间对Web服务QoS值进行建模。为了确定候选服务的QoS与用户QoS需求的符合程度,先针对服务的每个QoS属性,计算其与用户QoS需求的灰色区间关联系数;然后结合各个QoS属性的关联系数计算候选服务的QoS与用户QoS需求的综合灰色区间关联度,关联度越大的服务越符合用户的要求;最后从满足用户功能需求的Web服务中选择灰色关联度最大的Web服务推荐给用户。与其它Web服务评价模型相比较,该模型更加符合Web服务QoS的实际情况,能够在服务QoS信息不充分、不确定的环境下,对QoS属性进行合乎实际的分析处理,从而得到更加合理有效的QoS评价。     

QoS全局最优的多目标Web服务选择算法*

针对现有方法的不足,提出一种基于QoS全局最优的多目标动态Web服务选择算法。在给出动态服务组合模型的基础上,以“抽象服务规划”为输入,以用户的非功能性需求为全局约束,将动态服务选择问题转换为一个带QoS约束的多目标服务组合优化问题;利用多目标蚁群算法,多个目标函数被同时优化并产生一组满足约束条件的Pareto优化解。通过运用实验与基于多目标遗传算法的Web服务选择算法进行对比,证明了该方法的可行性和有效性。     

基于语义的QoS感知Web服务发现机制

随着提供相同功能的Web服务数量的日益增多,服务质量(Quality of Service,QoS)成为用户选择Web服务的重要考虑因素.目前,通过对服务QoS属性在语法层匹配来提供Web服务选取的机制不能很好地满足复杂QoS属性匹配的要求.研究了基于用户QoS需求偏好,将用户需求的QoS与候选服务的QoS进行语义比较,结合约束规划(Constraint Programming)方法,在语义层匹配Web服务的QoS属性,选取满足匹配要求的服务,最后对满足QoS属性值约束的候选服务进行优化选择处理,获取最终匹配的候选服务.     

Dynamic service selection with QoS constraints and inter-service correlations using cooperative coevolution

Building business processes by Web services in cloud computing has become the hotspot of service applications. Due to the complexity and uncertainty of business environment, QoS violations of service processes often take place at run-time. To rapidly recover from failures and minimize their impacts on the original execution plan of service processes, dynamic service selection is urgently needed once potential QoS violations are detected. However, existing research works do not fully investigate QoS constraints and inter-service correlations, as well as the breach penalty caused by service adjustment. In this paper, we present a new cooperative coevolutionary approach for dynamic service selection with QoS constraints and inter-service correlations. First, a novel formal model for the dynamic service selection problem with QoS constraints and inter-service correlations is presented. Second, a Double Information based Cooperative Coevolutionary algorithm (DICC) is proposed which uses Potter’s cooperative coevolutionary framework and provides both local and global knowledge for the dynamic service selection optimization. Finally, we develop a prototype system to apply our approach and adopt different test cases to show that our DICC approach performs more effectively and efficiently than existing algorithms.     

基于协同过滤的个性化Web服务选择方法

对传统的Web服务选择算法进行分析,并指出其在动态环境下存在的问题。为了解决该问题,提出了基于协同过滤算法的个性化Web服务选择方法,并设计了一个动态的Web服务选择架构,架构使用协同过滤算法对Web服务的服务质量(QoS)值进行预测并选择最优的Web服务以满足用户的需求。使用150万条真实Web服务的QoS数据与其他4类算法进行了对比实验,结果表明所提方法的可行性和准确性。     

一种基于服务质量的语义Web服务发现框架

在Web服务发现过程中,不同的用户希望根据自己的情况或个性化的需求进行服务选择,如何提供满足用户服务质量（QoS）需求的服务是语义Web服务应用急需解决的问题。基于OWL规范定义了一个QoS元模型——OWL-QoS,并将其应用于Web服务描述当中。根据提出的QoS元模型给出了一个基于QoS的语义Web服务发现框架,并从基本描述、IOPE、QoS三个层次实现了服务匹配。实验结果表明,与传统语义Web服务发现方法相比,该方法能显著提高服务匹配精度和效率。     

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.