Iterative selection algorithm for service composition in distributed environments

In service oriented architecture （SOA）, service composition is a promising way to create new services. However, some technical challenges are hindering the application of service composition. One of the greatest challenges for composite service provider is to select a set of services to instantiate composite service with end- to-end quality of service （QoS） assurance across different autonomous networks and business regions. This paper presents an iterative service selection algorithm for quality driven service composition. The algorithm runs on a peer-to-peer （P2P） service execution environment--distributed intelligent service execution （DISE）, which provides scalable QoS registry, dynamic service selection and service execution services. The most significant feature of our iterative service selection algorithm is that it can work on a centralized QoS registry as well as cross decentralized ones. Network status is an optional factor in our QoS model and selection algorithm. The algorithm iteratively selects services following service execution order, so it can be applied either before service execution or at service run-time without any modification. We test our algorithm with a series of experiments on DISE. Experimental results illustrated its excellent selection and outstanding performance.     

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。     

QoS-aware middleware for Web services composition

The paradigmatic shift from a Web of manual interactions to a Web of programmatic interactions driven by Web services is creating unprecedented opportunities for the formation of online business-to-business (B2B) collaborations. In particular, the creation of value-added services by composition of existing ones is gaining a significant momentum. Since many available Web services provide overlapping or identical functionality, albeit with different quality of service (QoS), a choice needs to be made to determine which services are to participate in a given composite service. This paper presents a middleware platform which addresses the issue of selecting Web services for the purpose of their composition in a way that maximizes user satisfaction expressed as utility functions over QoS attributes, while satisfying the constraints set by the user and by the structure of the composite service. Two selection approaches are described and compared: one based on local (task-level) selection of services and the other based on global allocation of tasks to services using integer programming.     

Broker-based SLA-aware composite service provisioning

QoS-aware service composition aims to satisfy users’ quality of services (QoS) needs during service composition. Traditional methods simply attempt to maximize user satisfaction by provisioning the composite service instance with the best QoS. These “best-effort” methods fail to take into account that there also exist other consumers competing for the service resources and their decisions of service selection/composition can impact on QoS. Since user's QoS needs can be met once the demanded level is reached, in this paper, we propose an “on-demand” strategy for QoS-aware service composition to replace the traditional “best-effort” strategy. The service broker is introduced to facilitate implementation of this strategy: it first purchases a number of service instances for each component from providers and then provisions the composite services with different QoS classes to consumers. This paper focuses on how the broker follows the service level agreement (SLA) to provision composite services in the “on-demand” manner. This problem is formally expressed as the minimization of the QoS distance function between SLA and QoS of composite service instances, under a series of constraints. Heuristic approaches are proposed for the problem and experiments are conducted at last to verify their effectiveness and efficiency.     

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.     

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。     

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 Requirement Generation and Algorithm Selection for Composite Service Based on Reference Vector

Under SOA (Service-Oriented Architecture), composite service is formed by aggregating multiple component services together in a given workflow. One key criterion of this research topic is QoS composition. Most work on service composition mainly focuses on the algorithms about how to compose services according to assumed QoS, without considering where the required QoS comes from and the selection of user preferred composition algorithm among those with different computational cost and di?erent selection resu...     

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.     

An adaptive algorithm for QoS-aware service composition in grid environments

Service composition enables users to realize their complex needs as a single request and it has been recognized as a flexible way for resource sharing and application integration since the appearance of Service-Oriented Architecture (SOA). For each of the needed individual services there may be many candidate services available presented by different vendors and with different functional and non-functional properties such as Quality of Service (QoS). Approaches are needed to select candidate services with various QoS levels according to user’s performance requirements meanwhile adapt to dynamic churn in grid environments. This paper mainly focuses on adaptive management of QoS-aware service composition in grid environments and proposes an adaptive algorithm for QoS-aware service composition (AQSC). In AQSC we model this problem as the Multi-Constrained Optimal Path selection problem (MCOP) and use heuristic approach for service selection, then backup services set is introduced as an adaptive mechanism so as to ensure the fulfillment of composite service when some candidate services fail or withdraw. Both theoretical analysis and simulation results indicate that AQSC has high composition success rate, finish rate and low cost.     

一种支持模糊QoS属性的服务组合容错方法

提出了一种支持模糊QoS属性的服务组合容错方法,运用区间数的方式来描述那些带布模糊性的QoS需求,针对服务组合的特点,构建了服务组合容错框架,通过对服务组合运行时的QoS历史数据和实测值进行偏差度量,重点讨论了在组件服务QoS失效和组合服务QoS失效这两种情况下的容错处理策略。实验结果证明了该方法的有效性。     

一种服务聚合中QoS全局最优服务动态选择算法

Web服务作为一种新型的Web应用模式,近年来得到了迅速的发展.如何动态地把现存的各种Web服务整合起来以形成新的、满足不同用户需求的、增值的复杂服务已成为新的应用需求和研究热点.针对现有服务聚合中服务选择技术的不足,提出了一种解决服务聚合中服务动态选择QoS全局最优化问题的实现算法GODSS(global optimal of dynamic Web services selection).算法的主要思想是把服务动态选择全局最优化问题转化为一个带QoS约束的多目标服务组合优化问题,利用多目标遗传算法的智     

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.     

自定义服务质量模型的网格工作流调度算法

随着网格服务应用的发展,在网格工作流中,复杂的任务可以由多个独立的服务,通过工作流引擎等方式组合成新服务后完成。在组合服务的过程中,由不同服务提供商提供的候选服务,具有不同的服务质量参数,在网格工作流调度中,需要满足用户定义的服务质量约束。提出了方便用户定义的服务质量模型,并且在该模型的基础上,改进了网格工作流调度算法,通过实验分析证明改进后的算法优于传统的调度算法。     

Self-adaptable media service architecture for guaranteeing reliable multimedia services

The main objective of this paper is to design and develop a Self-Adaptable Media Service Architecture (SAMSA) for providing reliable dynamic composite multimedia service through policy-based actions. The multimedia services such as media retrieval, transcoding, scaling and display services are combined based on the preferences of the user to create a dynamic composite multimedia service called as Video-on-Demand service. Such distributed multimedia services deployed using Service Oriented Architecture (SOA) can be accessed in heterogeneous environments that are prone to changes during run-time. To provide reliable and adaptive multimedia services, a powerful self-adaptable architecture with dynamic compositions of multimedia services is necessary to adapt during run-time and react to the environment. The adaptability in this proposed architecture is achieved by enabling the service providers to Monitor, Analyze and Act on the defined policies that support customization of compositions of multimedia services and guarantee the Quality of Service (QoS) provisioning. The Media Service Monitor (MSM) observes the business and quality metrics associated with the multimedia services during run-time. The monitored results are analyzed by Monitored Results Analyzer (MRA) which identifies the type and location of the fault. The Adaptive Media Service Manager (AMSM) takes corrective actions based on the monitored results, through the policies defined as an extension of WS-Policy (Web Service—Policy framework). The effectiveness of the proposed Self-Adaptable Media Service Architecture (SAMSA) has been evaluated on Dynamic Composite Real-time Video-on-Demand Web Service (DCRVoDWS) for a maximum of 200 simultaneous client’s requests. The analysis of results shows that the proposed architecture provides better improvement on reliability, response time and user satisfaction.     

A novel QoS model and computation framework in web service selection

With the rapid development of e-commerce over Internet, web services have attracted much attention in recent years. Nowadays, enterprises are able to outsource their internal business processes as services and make them accessible via the Web. Then they can dynamically combine individual services to provide new value-added services. With the increasing number of web services having equivalent functionality, the binding procedure is driven by some non-functional, Quality of Service (QoS) criteria, such as the money cost, response time, reputation, reliability or a trade-off between them. Thus, an important problem is, given QoS constraints, how to aggregate and leverage individual service??s QoS information to derive the optimal QoS of the composite service. In this paper, we propose a novel QoS model for performing flexible service selection. The key idea of the model is to relax users?? QoS constraints and try to find the most possible services satisfying users?? QoS requirements. Based on the proposed QoS framework, we develop various algorithms for making service selection on individual and composite services. We also introduce a top-k ranking strategy to reflect a user??s personalized requirements. Experimental evaluation shows the proposed QoS model is efficient and practical.     

A framework for QoS-aware binding and re-binding of composite web services

QoS-aware dynamic binding of composite services provides the capability of binding each service invocation in a composition to a service chosen among a set of functionally equivalent ones to achieve a QoS goal, for example minimizing the response time while limiting the price under a maximum value.This paper proposes a QoS-aware binding approach based on Genetic Algorithms. The approach includes a feature for early run-time re-binding whenever the actual QoS deviates from initial estimates, or when a service is not available. The approach has been implemented in a framework and empirically assessed through two different service compositions.     

基于免疫遗传算法的Web服务组合方法

针对Web服务组合提出一种免疫遗传算法(IGA)。该算法将免疫原理引入遗传算法(GA)中,提高算法的整体特性。主要表现在免疫选择可有效地防止早熟,基于免疫记忆的子群体信息交换策略可加速收敛。GA在Web服务选择上存在不足,而IGA可以在备选的Web服务中进行有效的选择并最终组成Web服务组合,提高服务组合的质量和收敛速度。仿真实验结果表明IGA比GA更有效。     

Research on Web service selection based on cooperative evolution

Web service selection, as an important part of Web service composition, has direct influence on the quality of composite service. Therefore, it has attracted many researchers to focus on the research of quality of service (QoS) driven Web service selection in the past years, and many algorithms based on integer programming (IP), mixed integer linear programming (MILP), multi-dimension multi-choice 0–1 knapsack problem (MMKP), Markov decision programming (MDP), genetic algorithm (GA), and particle swarm optimization (PSO) and so on, have been presented to solve it, respectively. However, these results have not been satisfied at all yet. In this paper, a new cooperative evolution (Co-evolution) algorithm consists of stochastic particle swarm optimization (SPSO) and simulated annealing (SA) is presented to solve the Web service selection problem (WSSP). Furthermore, in view of the practical Web service composition requirements, an algorithm used to resolve the service selection with multi-objective and QoS global optimization is presented based on SPSO and the intelligent optimization theory of multi-objective PSO, which can produce a set of Pareto optimal composite services with constraint principles by means of optimizing various objective functions simultaneously. Experimental results show that Co-evolution algorithm owns better global convergence ability with faster convergence speed. Meanwhile, multi-objective SPSO is both feasible and efficient.     

满足原子事务的QoS感知的自适应服务选择

随着云计算的兴起,服务计算成为其实现的一种关键技术,学术领域对服务组合有新的思考。提出了满足原子事务的服务质量（quality of service,QoS）感知的自适应服务选择方法,既考虑QoS约束,也考虑服务间的事务约束,在进行全局优化选择时结合适当的协调模式选择策略,以进一步提高可靠性。首先制定自适应规则和建立QoS模型,并引用其他学者关于事务构建和规约规则的模型。接着分析事务与QoS约束下自适应服务选择算法机制。最后通过实验评价了方法的有效性和健壮性。