WEB SERVICE SELECTION ALGORITHM BASED ON PRINCIPAL COMPONENT ANALYSIS

Existing Web service selection approaches usually assume that preferences of users have been provided in a quantitative form by users. However, due to the subjectivity and vagueness of preferences, it may be impractical for users to specify quantitative and exact preferences. Moreover, due to that Quality of Service (QoS) attributes are often interrelated, existing Web service selection approaches which employ weighted summation of QoS attribute values to compute the overall QoS of Web services may produce inaccurate results, since they do not take correlations among QoS attributes into account. To resolve these problems, a Web service selection framework considering user's preference priority is proposed, which incorporates a searching mechanism with QoS range setting to identify services satisfying the user's QoS constraints. With the identified service candidates, based on the idea of Principal Component Analysis (PCA), an algorithm of Web service selection named PCA-WSS (Web Service Selection based on PCA) is proposed, which can eliminate the correlations among QoS attributes and compute the overall QoS of Web services accurately. After computing the overall QoS for each service, the algorithm ranks the Web service candidates based on their overall QoS and recommends services with top QoS values to users. Finally, the effectiveness and feasibility of our approach are validated by experiments, i.e. the selected Web service by our approach is given high average evaluation than other ones by users and the time cost of PCA-WSS algorithm is not affected acutely by the number of service candidates.

Toward a More Accurate Web Service Selection Using Modified Interval DEA Models with Undesirable Outputs

With the growing number of Web services on the internet,there is a challenge to select the best Web service which can offer more quality-of-service(QoS)values at the lowest price.Another challenge is the uncertainty of QoS values over time due to the unpredictable nature of the internet.In this paper,we modify the interval data envelopment analysis(DEA)models[Wang,Greatbanks and Yang(2005)]for QoS-aware Web service selection considering the uncertainty of QoS attributes in the presence of desirable and undesirable factors.We conduct a set of experiments using a synthesized dataset to show the capabilities of the proposed models.The experimental results show that the correlation between the proposed models and the interval DEA models is significant.Also,the proposed models provide almost robust results and represent more stable behavior than the interval DEA models against QoS variations.Finally,we demonstrate the usefulness of the proposed models for QoS-aware Web service composition.Experimental results indicate that the proposed models significantly improve the fitness of the resultant compositions when they filter out unsatisfactory candidate services for each abstract service in the preprocessing phase.These models help users to select the best possible cloud service considering the dynamic internet environment and they help service providers to improve their Web services in the market.

User-preferences based service selection model in large-scale services with various QoS properties

With the rapid growth of service scale, there are many services with the same functional properties but different non-flmctional properties on the Internet. There have been some global optimizing service selection algorithms for service selection. However, most of those approaches cannot fully reflect users＇ preferences or are not fully suitable for large-scale services selection. In this paper, an ant colony optimization （ACO） algorithm for the model of global optimizing service selection with various quality of srevice （QoS） properties is employed, and a user-preference based large-scale service selection algorithm is proposed. This algorithm aims at optimizing user-preferred QoS properties and selecting services that meet all user-defined QoS thresholds. Experiment results prove that this algorithm is very efficient in this regard.

Evaluation of Parameters Importance in Cloud Service Selection Using Rough Sets

With the rapid development of the cloud computing technology, it has matured enough for a lot of individuals and organizations to move their work into the cloud. Correspondingly, a variety of cloud services are emerging. It is a key issue to assess the cloud services in order to help the cloud users select the most suitable cloud service and the cloud providers offer this service with the highest quality. The criteria parameters defining the cloud services are complex which lead to cloud service deviation. In this paper, we propose an assessment method of parameters importance in cloud services using rough set theory. The method can effectively compute the importance of cloud services parameters and sort them. On the one hand, the calculation can be used as the credible reference when users choose their appropriate cloud services. On the other hand, it can help cloud service providers to meet user requirements and enhance the user experience. The simulation results show the effectiveness of the method and its relevance in the cloud context.

Qo S-Based Service Selection with Lightweight Description for Large-Scale Service-Oriented Internet of Things

Quality of Service （QoS）-based service selection is the key to large-scale service-oriented Internet of Things （lOT）, due to the increasing emergence of massive services with various QoS. Current methods either have low selection accuracy or are highly time-consuming （e.g., exponential time complexity）, neither of which are desirable in large-scale lOT applications. We investigate a QoS-based service selection method to solve this problem. The main challenges are that we need to not only improve the selection accuracy but also decrease the time complexity to make them suitable for large-scale lOT applications. We address these challenges with the following three basic ideas. First, we present a lightweight description method to describe the QoS, dramatically decreasing the time complexity of service selection. Further more, based on this QoS description, we decompose the complex problem of QoS-based service selection into a simple and basic sub-problem. Finally, based on this problem decomposition, we present a QoS-based service matching algorithm, which greatly improves selection accuracy by considering the whole meaning of the predicates. The traces-driven simulations show that our method can increase the matching precision by 69% and the recall rate by 20% in comparison with current methods. Moreover, theoretical analysis illustrates that our method has polynomial time complexity, i.e., O（m^2 × n）, where m and n denote the number of predicates and services, respectively.

A Multi-dimensional Trust-aware Cloud Service Selection Mechanism Based on Evidential Reasoning Approach

In the last few years, cloud computing as a new computing paradigm has gone through significant development, but it is also facing many problems. One of them is the cloud service selection problem. As increasingly boosting cloud services are offered through the internet and some of them may be not reliable or even malicious, how to select trustworthy cloud services for cloud users is a big challenge. In this paper, we propose a multi-dimensional trust-aware cloud service selection mechanism based on evidential reasoning(ER) approach that integrates both perception-based trust value and reputation based trust value, which are derived from direct and indirect trust evidence respectively, to identify trustworthy services. Here, multi-dimensional trust evidence, which reflects the trustworthiness of cloud services from different aspects, is elicited in the form of historical users feedback ratings. Then, the ER approach is applied to aggregate the multi-dimensional trust ratings to obtain the real-time trust value and select the most trustworthy cloud service of certain type for the active users. Finally, the fresh feedback from the active users will update the trust evidence for other service users in the future.

Indeterminacy-aware service selection for reliable service composition

With the development of Intemet and Web service technology, Web service composition has been an effective way to construct software applications; service selection is the crucial element in the composition process. However, the existing selection methods mostly generate static plans since they neglect the inherent stochastic and dynamic nature of Web services. As a result, Web service composition often inevitably terminates with failure. An indeterminacy-aware service selection algorithm based on an improved Markov decision process （IMDP） has been designed for reliable service composition, but it suffers from higher computation complexity. Therefore, an efficient method is proposed, which can reduce the computation cost by converting the service selection problem based on IMDP into solving a nonhomogeneous linear equation set. Experimental results demonstrate the success rate of service composition has been improved greatly, whilst also reducing computation cost.

Dynamic Web service selection group decision-making based on heterogeneous QoS models

In open and dynamic Internet environment, multi-periods quality of service （QoS）, multiple decision-makers based on heterogeneous QoS models and uncertainty weights make optimal Web service selection hard. To solve the difficulties above, dynamic Web service selection group decision-making based on heterogeneous QoS models （DWSSGD_HQM） is proposed. Drawing on and extending technique for order preference by similarity to ideal solution （TOPSIS）, DWSSGD_HQM supports multiple decision-makers based on heterogeneous QoS models expressed in real numbers, interval numbers, triangular fuzzy numbers and intuitionistic fuzzy numbers respectively, and takes multi-periods QoS and weights of user and group into account. Six steps are detailed. They are converting heterogeneous QoS models into ones expressed in interval numbers, calculating weighted normalized decision-matrix, determining the group positive-ideal and negative-ideal solutions, calculating the close-degrees of candidates, aggregating close-degrees of multi-periods for each decision-maker, and ranking the alternatives. Finally, experiments are conducted by using actual QoS data to evaluate the effectiveness of the proposed approach.

Solving Service Selection Problem Based on a Novel Multi-Objective Artificial Bees Colony Algorithm

Service computing is a new paradigm and has been widely used in many fields. The multi-objective service selection is a basic problem in service computing and it is non-deterministic polynomial(NP)-hard. This paper proposes a novel multi-objective artificial bees colony(n-MOABC) algorithm to solve service selection problem. A composite service instance is a food source in the algorithm. The fitness of a food source is related to the quality of service(QoS) attributes of a composite service instance. The search strategy of the bees are based on dominance. If a food source has not been updated in successive maximum trial(Max Trial) times, it will be abandoned. In experiment phase, a parallel approach is used based on map-reduce framework for n-MOABC algorithm. The performance of the algorithm has been tested on a variety of data sets. The computational results demonstrate the effectiveness of our approach in comparison to a novel bi-ant colony optimization(NBACO)algorithm and co-evolution algorithm.

Research of service selection algorithm for net-centric simulation task community

Being a new-generation C4ISR system simulation method,the construction approach of net-centric simulation(NCS)is developing toward net-centric from the traditional approach of platform-centric.NCS is mainly completed by the construction of the simulation task community(STC),the key to which being the dynamic integration of the various services spread in the network in order to form a new STC that meets the requirements of different users.In this study,a simulation task community service selection algorithm(STCSSA)is proposed.The main idea of this algorithm is to transform the construction of STC to the searching of optimal multi-objectives services with QoS global constraints.This paper first introduces the QoS model of STC and evaluates the service composition process,then presents the detailed operating process of STCSSA and design of the dynamic inertia weight strategy of the algorithm,and also proposes an optional variation method.Comparative tests were performed on STCSSA with other particle swarm optimization algorithms.It was validated from the perspective of performance that the proposed algorithm has advantages in improving the rate of convergence and avoiding local optimum,and from the perspective of practical application STCSSA also demonstrated feasibility in the construction of large-scale NCS task community.

A Load-Fairness Prioritization-Based Matching Technique for Cloud Task Scheduling and Resource Allocation

In a cloud environment,consumers search for the best service provider that accomplishes the required tasks based on a set of criteria such as completion time and cost.On the other hand,Cloud Service Providers(CSPs)seek to maximize their profits by attracting and serving more consumers based on their resource capabilities.The literature has discussed the problem by considering either consumers’needs or CSPs’capabilities.A problem resides in the lack of explicit models that combine preferences of consumers with the capabilities of CSPs to provide a unified process for resource allocation and task scheduling in a more efficient way.The paper proposes a model that adopts a Multi-Criteria Decision Making(MCDM)method,called Analytic Hierarchy Process(AHP),to acquire the information of consumers’preferences and service providers’capabilities to prioritize both tasks and resources.The model also provides a matching technique to assign each task to the best resource of a CSP while preserves the fairness of scheduling more tasks for resources with higher capabilities.Our experimental results prove the feasibility of the proposed model for prioritizing hundreds of tasks/services and CSPs based on a defined set of criteria,and matching each set of tasks/services to the best CSPS.

面向工业互联网平台的二维制造服务协作优化

工业互联网平台被公认为智能制造的必要推动者,使物理制造资源得以虚拟化,并允许资源以服务的形式进行协作。作为平台的核心功能,制造服务协作优化致力于为制造任务提供高质量的服务协作解决方案。这种优化与任务的功能和数量要求密不可分,在编排服务时必须满足这些要求。然而,现有的制造服务协作优化方法主要关注服务之间针对功能需求的横向协作,很少考虑纵向协作来覆盖所需的数量。为了解决这一差距,本文提出了一种结合功能和数量协作的二维服务协作方法。首先,提出了一种描述服务的多粒度制造服务建模方法。在此基础上,建立了二维制造服务协同优化模型。在垂直维度上,多个功能等效的服务组成一个服务集群来完成一个子任务;在水平维度上,互补服务集群协作完成整个任务。服务的选择和所选服务的金额分配是模型中的关键问题。为了解决这个问题,设计了一种具有多个局部搜索算子的多目标模因算法。将该算法嵌入竞争机制来动态调整本地搜索算子的选择概率。实验结果表明,与常用算法相比,该算法在收敛性、解质量和综合度量方面具有优势。

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.

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 different selection results. In this paper, we propose to strengthen current service composition mechanism by generation of QoS requirement and its algorithm selection based on the QoS reference vectors which are calculated optimally from the existing individual services＇ QoS by registry to represent QoS overview about the best QoS, the worst （or most economical） QoS, or the average QoS of all composite services. To implement QoS requirement, which is determined according to QoS overview, this paper introduces two selection algorithms as two kinds of experiment examples, one aiming at the most accurate service selection and the other chasing for trade-off between selection cost and result. Experimental results show our mechanism can help the requester achieve his expected composite service with appropriate QoS requirement and customized selection algorithm.

A synergetic formed quantitative model for users' selections in different network service providers

Multiple heterogeneous wireless techniques, possibly administered by different entities, are coexisted in the rapidly-expanding communications market. In order to understand the complex interactions among different network service providers（NSPs）, it is important to study the factors taken into consideration by users during their selections of the NSPs. The article focuses on interactions between macro-level dynamics of user subscription and the factors causing such dynamics. The key factor was discussed on the interactions between different subpopulations rather than the general factors such as quality of service（Qo S）. Guided by synergetic theory, a general mathematical description model was built. The influence of key factors on users＇ selections was considered and analyzed. Some examples were presented to validate capabilities of the proposed model.

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.

FEWSS-FRAMEWORK TO EVALUATE THE SERVICE SUITABILITY AND PRIVACY IN A DISTRIBUTED WEB SERVICE ENVIRONMENT

This paper focuses on addressing the problem of web service monitoring by evaluating its suitability and privacy in a distributed web service environment(DWS).The need for web services monitoring and evaluation is necessary because the quantity and quality of retrieved web services generally does not fulfill the demands or requirements of the web service requesters.In the literature,many authors proposed suitable solutions for checking the quality of web service in an ad hoc scenario but there is no available testbed for this purpose.In this work,it is proposed to develop a framework as a testbed for evaluating the web service for its suitability and privacy.In order to verify,the retrieved web services fulfill the demands and requirements of the requester in a DWS environment.The Framework to Evaluate the Web Service Suitability(FEWSS)supports service providers in modeling the services with testbed and to design service behavior to comply the service suitability.It generates run-time instances of web services,client’s requests,service registries and other entities in order to emulate realistic SOA environments.By generating a real testbed,our approach assists in runtime test for provider system/services.Particular focus has been put on the privacy policy extensibility to allow the service providers and users in a complex environment.FEWSS also provides an intuitive interface for testing all services under SOA control.