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.     

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.     

Comparative analysis of multi-objective evolutionary algorithms for QoS-aware web service composition

Web service composition combines available services to provide new functionality. The various available services have different quality-of-service (QoS) attributes. Building a QoS-optimal web service composition is a multi-criteria NP-hard problem. Most of the existing approaches reduce this problem to a single-criterion problem by aggregating different criteria into a unique global score (scalarization). However, scalarization has some significant drawbacks: the end user is supposed to have a complete a priori knowledge of its preferences/constraints about the desired solutions and there is no guarantee that the aggregated results match it. Moreover, non-convex parts of the Pareto set cannot be reached by optimizing a convex weighted sum. An alternative is to use Pareto-based approaches that enable a more accurate selection of the end-user solution. However, so far, only few solutions based on these approaches have been proposed and there exists no comparative study published to date. This motivated us to perform an analysis of several state-of-the-art multi-objective evolutionary algorithms. Multiple scenarios with different complexities are considered. Performance metrics are used to compare several evolutionary algorithms. Results indicate that GDE3 algorithm yields the best performances on this problem, also with the lowest time complexity.     

GOS: a global optimal selection strategies for QoS-aware web services composition

Services composition technology provides a promising way to create new services in services-oriented architecture. However, some challenges are hindering the application of services composition. One of the major challenges encountered by composite services developer is how to effectively select a set of services across different autonomous regions (e.g., organization or business) to instantiate a composite service which can satisfy user’s QoS constraints. To solve QoS-aware Web service composition problem, this paper proposes a global optimization selection (GOS) approach based on prediction mechanism for QoS values of local services. The GOS includes two parts. First, the local preprocessing service selection algorithm can be used to increase composite services performance in run-time by predicting the change of service quality parameters. Second, GOS aims at enhancing the run-time performance of global selection by reducing QoS aggregation operations. The simulation results show that the GOS has excellent selection and lower execution cost than existing approaches.     

QoS-aware and multi-granularity service composition

Composition of Web services can be very complex, and usually involves multiple atomic services and varieties of message exchange patterns. Worst still, with the increasing amount of available services with varying granularity and quality, selecting the best combination of services becomes very challenging. This paper addresses the issues on multi-granularity service composition with awareness of the service quality. In particular, we consider how a new service composition plan is produced, while preserving its original observable behaviors of a service that are shown to the service user, by substituting the service with another service or a set of services of finer or coarser grain. The new plan aims to have services of better quality (if the corresponding underlying services are available). To achieve this, we firstly define a behavioral signature model to capture observable behaviors of services. We then present that two service composition plans are choreography equivalent if they comply with the same behavioral signature model. We then propose a behavioral extracting algorithm to obtain the behavioral signature model from a service composition plan. We also present a method to determine choreography equivalence. Finally we briefly describe our prototype implementation that captures all these proposed algorithms.     

Consumer-centric QoS-aware selection of web services

There exist many web services which exhibit similar functional characteristics. It is imperative to provide service consumers with facilities for selecting required web services according to their non-functional characteristics or quality of service (QoS). However, the selection process is greatly complicated by the distinct views of service providers and consumers on the services QoS. For instance, they may have distinct views of the service reliability—wherein a consumer considers that a service is reliable if its success rate is higher than 99%, while a provider may consider its service as reliable if its success rate is higher than 90%. The aim of this paper is to resolve such conflicts and to ensure consensus on the QoS characteristics in the selection of web services. It proposes a QoS Consensus Moderation Approach (QCMA) in order to perform QoS consensus and to alleviate the differences on QoS characteristics in the selection of web services. The proposed approach is implemented as a prototype tool and is tested on a case study of a hotel booking web service. Experimental results show that the proposed approach greatly improves the service selection process in a dynamic and uncertain environment of web services.     

QoS感知的语义服务组合搜索算法

针对如何在海量Web服务中进行快速的服务自动组合、以满足客户复杂需求的问题,提出一种基于规划图思想的搜索组合算法,该算法在正向扩展阶段除了依据所选取服务的功能与非功能属性进行有效剪枝外,还实时级联更新已驱动服务的QoS信息并有效记录所操作服务的前驱服务,通过前驱索引集合,确保逆向求解阶段的效率并满足了QoS最优的约束。在实验环节证明了注册服务数量与求解效率的线性关系,以及与其他算法相比较所体现出来的性能优势。     

一种高效的服务组合优化算法

随着功能性属性相同而非功能性属性各异的Web服务的大量涌现,如何在服务组合业务流程中为各个任务选择相应的组件服务以达到组合服务的QoS(quality of service)最大化,并在此基础上满足不同用户的需求,已成为了国内外研究的热点.由于该问题的复杂性(NP-hard),目前存在的大多数方法都并不十分适合需要相对精确、实时决策的Web服务组合系统.因此,本文提出了一种基于凸包构建的组合服务优化算法(CM-HEU)用以解决QoS感知的服务组合优化问题.CM-HEU首先通过对组合服务中的每组任务进行凸包构建,以减少搜索空间.然后通过对初始解向量的多次升级和一次降级操作以达到全局优化的目标.实验表明:相对于现阶段存在的一些主流方法,CM-HEU不仅能得到一个比较理想的结果,并且具有良好的效率.     

QoS-aware service composition for distributed video surveillance

Quality of Service (QoS) is essential for the ubiquitous access of media services in real-time distributed video surveillance applications. To have ubiquitous access of desired media with emergency officials’ handheld devices, appropriate media transcoding services are required. Currently, it is challenging to select and compose these services for each of the devices to satisfy the desired QoS demand. To compose these media services so that video stream is available for target pervasive and smart devices, a composition algorithm is required. Thus, this paper presents a QoS-aware service composition algorithm to select the best composition for the target ubiquitous client so that it can optimally provide QoS to heterogeneous users. We have implemented a video surveillance prototype to demonstrate the performance of the proposed QoS-aware composition algorithm. Results from this prototype reveal that the approach is suitable for real-time video surveillance.     

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。     

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.     

基于QoS的组合服务优化选择问题建模与求解

提出了一种支持Web服务动态组合的框架,并在此基础上对Web服务组合中的服务优化选择问题建模,根据用户设置的QoS约束条件,将服务优化选择问题分成3类,并分别给出了相应的服务选择算法。实验证明算法在保证一定优度的同时亦具有良好的时间性能。     

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.     

A novel heuristic algorithm for QoS-aware end-to-end service composition

Many works have been carried out to find the efficient algorithms for QoS-aware service composition in recent years. Nevertheless, on one hand, some of these works only consider the local QoS attributes in Web services composition; on the other hand, some ideas derived from QoS selection algorithms for network routing are directly applied in service composition without any adaption. A service composition model with end-to-end QoS constraints has been presented in this paper. An improved heuristics HCE based on the observation of characteristic of end-to-end service composition is proposed as a novel solution. Simulation results reveal the better performance of proposed heuristic compared to the other two heuristics, HMCOP and generic CE algorithm.     

基于AOP的Web服务组合容错模型

为了解决现有Web服务组合容错方案中功能性代码与容错处理代码缠绕问题,提出了基于AOP的Web服务组合容错模型.对Web服务组合执行过程的各阶段失效原因进行了分析和归类,给出了相应的容错处理策略;基于AOP横切关注点分离的思想,构建了Web服务组合容错模型,保障执行过程中业务功能和服务质量的同时,实现了容错关注点与功能关注点的分离.通过实例分析,验证了该模型的可行性和有效性.     

QoS感知的云服务优化组合研究

为了更好地解决服务质量感知的云服务优化组合问题,首先对社会认知算法进行了改进,提出了面向离散型优化问题的模仿学习方法以及基于多次变异的观察学习方法。然后使用改进的社会认知算法对服务质量感知的云服务优化组合问题进行了求解。实验结果表明,改进的社会认知算法具有较强的搜索能力和较快的收敛速度,并且具有较强的推广性,可以用来求解其他离散型优化问题。     

QoS-aware web services composition using GRASP with Path Relinking

In service oriented scenarios, applications are created by composing atomic services and exposing the resulting added value logic as a service. When several alternative service providers are available for composition, quality of service (QoS) properties such as execution time, cost, or availability are taken into account to make the choice, leading to the creation of QoS-aware composite web services. Finding the set of service providers that result in the best QoS is a NP-hard optimization problem. This paper presents QoS-Gasp, a metaheuristic algorithm for performing QoS-aware web service composition at runtime. QoS-Gasp is an hybrid approach that combines GRASP with Path Relinking. For the evaluation of our approach we compared it with related metaheuristic algorithms found in the literature. Experiments show that when results must be available in seconds, QoS-Gasp improves the results of previous proposals up to 40%. Beside this, QoS-Gasp found better solutions than any of the compared techniques in a 92% of the runs when results must be available in 100 ms; i.e., it provides compositions with a better QoS, implying cost savings, increased availability and reduced execution times for the end-user.