Service Composition Method Research Articles

A new fuzzy QoS-aware manufacture service composition method using extended flower pollination algorithm

With the increasing complexity of manufacturing tasks and the exponential growth of candidate services, manufacturing service composition has become considerably challenging in relation to the integration of service supply chains in fuzzy manufacturing environments. Quality of service (QoS), as a popular index, is widely used to evaluate the fitness of solutions to the manufacturing service composition (SMSC). In this study, we first establish a new fuzzy QoS-aware mathematical model that considers the preferences of manufacturing enterprises by assigning different sub-tasks with different weights to evaluate the global fuzzy QoS of the SMSCs. We then extend the flower pollination algorithm (FPA) to obtain an optimal SMSC more effectively by making the switch probability self-adaptive, improving the local search ability, and adding the strategy of elite replacement. Finally, we demonstrate that the proposed extended FPA is an effective and efficient algorithm for solving the manufacturing service composition problem with differently weighted sub-tasks in a fuzzy manufacturing environment. We do this by comparing it with other well-known metaheuristic algorithms such as basic FPA, genetic algorithm, cuckoo search algorithm, and particle swarm optimization.

Future Intelligent Systems and Networks

Future Intelligent Systems and Networks

A clustering network-based approach to service composition in cloud manufacturing

Cloud manufacturing (CMfg) is a new business paradigm that aims to provide manufacturing resources as services over the Internet in a convenient pay-as-you-go mode. Service composition is a critical means for achieving value adding and synergy of resources in CMfg. There are a vast number of services in a CMfg platform, which makes traditional service composition methods incapable and low efficiency in solving the problem of service composition. Most of existing research on CMfg was devoted to the optimal selection of services under a specific composition flow and a group of given candidate pools. Hence, there is a need to explore effective approaches to obtaining service composition flow and candidate sets. The network-based web service composition approach has recently drawn much attention and has been proved effective in coping with the challenge of large-scale services. Inspired by this, a novel approach called service clustering network-based service composition is proposed in this paper. In this method, services are first clustered into abstract services, and then a clustering network of the abstract services is established. In this way, service composition paths and corresponding candidate sets for fulfilling manufacturing tasks can be quickly obtained without requiring task decomposition, which decreases the difficulty and improves efficiency of service composition. Effectiveness and efficiency of the approach are verified through simulation experiments.

Cross-organizational Collaborative Web Service Composition Method Based on Local Quality Constraint Resolution

Cross-organizational Collaborative Web Service Composition Method Based on Local Quality Constraint Resolution

Hybrid teaching–learning-based optimization of correlation-aware service composition in cloud manufacturing

Cloud manufacturing (CMfg) provides a new product development model in which users are enabled to configure, select, and utilize customized manufacturing services on-demand. The service composition with optimal overall quality of service (QoS) is of great significance since it affects the efficiency of resource sharing and optimal allocation in CMfg systems. However, some limitations still exist in manufacturing service composition methods especially in the complicated cloud environment. This owes to two aspects: (1) current service composition approaches assume that the QoS of services is fixed and not influenced by other services, the existence of the service correlation context is not considered adequately, thus resulting in no accordance with practical applications; (2) with the growing number of candidate services in cloud resource pools, the traditional methods might be inefficient for addressing large scale service composition problems. To overcome such drawbacks, this study proposes a new hybrid teaching–learning-based optimization (HTLBO) algorithm for optimal service composition with the consideration of service correlations. Experiments are conducted to verify the effectiveness and feasibility of the proposed algorithm.

A semantic web-based framework for service composition in a cloud manufacturing environment

Cloud manufacturing has been recognised as a transformative manufacturing paradigm to enable rapid production of highly customised products in a networked environment, through on-demand consumption of cloud-based manufacturing services. The fundamental issue of on-demand manufacturing service provision is service composition, where distributed manufacturing resources are mapped to personalised service requests. This paper examines knowledge-based service composition and adaptive resource planning in a cloud manufacturing environment. The intention is to develop an integrated networked environment, allowing fast resource allocation for a given service request, subject to governance policies, resource access policies, resource availability information and etcetera. The research challenge in this is to explore a feasible service composition method that facilitates easy mapping between service requests and manufacturing resources based on restrictive rule sets in the cloud and availability information about a resource. The research work in this paper analyses the relevant research challenges, proposes a practical approach and implements the solution in the form of a web-based system. The proposed system utilises distributed knowledge for intelligent service composition and adaptive resource planning. A case study is also presented to validate the performance of the proposed approach.

A Reliability Calculation Method for Web Service Composition Using Fuzzy Reasoning Colored Petri Nets and Its Application on Supercomputing Cloud Platform

In order to develop a Supercomputing Cloud Platform (SCP) prototype system using Service-Oriented Architecture (SOA) and Petri nets, we researched some technologies for Web service composition. Specifically, in this paper, we propose a reliability calculation method for Web service compositions, which uses Fuzzy Reasoning Colored Petri Net (FRCPN) to verify the Web service compositions. We put forward a definition of semantic threshold similarity for Web services and a formal definition of FRCPN. We analyzed five kinds of production rules in FRCPN, and applied our method to the SCP prototype. We obtained the reliability value of the end Web service as an indicator of the overall reliability of the FRCPN. The method can test the activity of FRCPN. Experimental results show that the reliability of the Web service composition has a correlation with the number of Web services and the range of reliability transition values.

Automatic Web Service Composition Based on Uncertainty Execution Effects

By arranging multiple existing web services into workflows to create value-added services, automatic web service composition has received much attention in service-oriented computing. A large number of methods have been proposed for it although most of them are merely based on the matching of input-output parameters of services. Besides these parameters, some other elements can affect the execution of services and their composition, such as the preconditions and service execution results. In particular, the execution effects of some services are often uncertain because of the complex and dynamically changing application environments in the real world, and this can cause the emergence of nondeterministic choices in the workflows of composite services. However, the previous methods for automatic service composition mainly rely on sequential structures, which make them difficult to take into account uncertain effects during service composition. In this paper, Graphplan is employed and extended to tackle this problem. In order to model services with uncertain effects, we first extend the original form of Graphplan. Then, we propose a novel approach that can introduce branch structures into composite solutions to cope with such uncertainty in the service composition process. Extensive experiments are performed to evaluate and analyze the proposed methodology.

PRAGMATICS DRIVEN LAND COVER SERVICE COMPOSITION UTILIZING BEHAVIOR-INTENTION MODEL

Web service composition is one of the key issues to develop a global land cover (GLC) information service portal. Aiming at the defect that traditional syntax and semantic service compositionare difficult to take pragmatic information into account, the paper firstly analyses three tiers of web service language and their succession relations, discusses the conceptual model of pragmatic web service, and proposes the idea of pragmatics-oriented adaptive composition method based on the analysis of some examples. On this basis it puts forward the pragmatic web service model based on Behavior-Intention through presetting and expression of service usability, users' intention, and other pragmatic information, develops the on-demand assembly method based on the agent theory and matching and reconstruction method on heterogeneous message, solves the key technological issue of algorithm applicability and heterogeneous message transformation in the process of covering web service composition on the ground, applies these methods into service combination, puts forward the pragmatic driven service composition method based on behavior-intention model, and effectively settles the issue of coordination and interaction of composite service invocation.

PRAGMATICS DRIVEN LAND COVER SERVICE COMPOSITION UTILIZING BEHAVIOR-INTENTION MODEL

Abstract. Web service composition is one of the key issues to develop a global land cover (GLC) information service portal. Aiming at the defect that traditional syntax and semantic service compositionare difficult to take pragmatic information into account, the paper firstly analyses three tiers of web service language and their succession relations, discusses the conceptual model of pragmatic web service, and proposes the idea of pragmatics-oriented adaptive composition method based on the analysis of some examples. On this basis it puts forward the pragmatic web service model based on Behavior-Intention through presetting and expression of service usability, users' intention, and other pragmatic information, develops the on-demand assembly method based on the agent theory and matching and reconstruction method on heterogeneous message, solves the key technological issue of algorithm applicability and heterogeneous message transformation in the process of covering web service composition on the ground, applies these methods into service combination, puts forward the pragmatic driven service composition method based on behavior-intention model, and effectively settles the issue of coordination and interaction of composite service invocation.

Recommendation for Web Service Composition by Mining Usage Logs

Web service composition has been one of the most researched topics of the past decade. Novel methods of web service composition are being proposed in the literature include Semantics-based composition, WSDLbased composition. Although these methods provide promising results for composition, search and discovery of web service based on QoS parameter of network and semantics or ontology associated with WSDL, they do not address composition based on usage of web service. Web Service usage logs capture time series data of web service invocation by business objects, which innately captures patterns or workflows associated with business operations. Web service composition based on such patterns and workflows can greatly streamline the business operations. In this research work, we try to explore and implement methods of mining web service usage logs. Main objectives include Identifying usage association of services. Linking one service invocation with other, Evaluation of the causal relationship between associations of services

A Novel Rubric and Feature-based Appraisaland Comparison Framework for the Evaluationof Semantic Web Services Composition Approaches

Background/Objectives: This article proposes a novel methodology based on rubrics and feature-based schemes for the appraisal and comparison of approaches to semantic Web services composition and discovery. Methods/Statistical analysis: In order to evaluate the Semantic Web services composition and discovery approaches we created a new framework called RFSWS. This framework is the combination of traditional feature-based evaluation schemes and newly developed analytic rubrics tables. Five recently introduced prominent Semantic Web services composition approaches were identified, explained, and then evaluated/compared using RFSWS. Findings: In this work we determined aspects of Semantic Web services composition and discovery processes that can be evaluated as performance criteria in rubric tables. This is a novel application of rubrics, which have traditionally been used for grading student performance by teachers. We created a novel framework called RFSWS consisting of rubric tables and a feature-based evaluation scheme for the evaluation and comparison of Semantic Web service discovery and composition approaches, and applied it in the evaluation of five recently introduced prominent Semantic Web services composition approaches. Considering the shortcomings of existing Semantic Web services composition approaches that were discovered through this evaluation, we proposed an idealized dynamic Semantic Web service discovery and composition method, a yardstick by which all future Semantic Web services composition approaches can be evaluated. Application/ Improvements: Our novel RFSWS framework can be applied in the comprehensive and systematic evaluation/comparison of Semantic Web services discovery and composition approaches.

Trust Based Cloud Service Composition Framework

Cloud Computing plays an important role in academic and industry. Business process are composed and executed in the distributed loosely coupled environment. Business processes are implemented as the composite service which involves many services and these services connected by different workflow patterns. Numerous functionally similar services are available in the cloud. As it is an on-demand service, cloud service consumer needs some Quality of Service factors in services. Trust plays an import role in service selection. Selecting the trust worthy service for service composition with the cost and other constraints is a tedious task. In this paper, service trust estimation method based on Beta distribution is proposed. Trust based Cloud service selection framework for service composition is proposed and it is proved that the proposed framework provides better performance as compared to the well-known service composition methods based on the execution time and optimality value.

SERVICE COMPOSITION MECHANISMS IN THE MULTI-CLOUD ENVIRONMENTS: A SURVEY

Cloud computing is a serious technological creation in the area of information systems development that supplies the benefits of self-service applications, source mixing, and wide network access. Nowadays, cloud computing to support fastness, easy combination, and low-cost of distributed applications in dissimilar environments have become increasingly famous. Therefore, service combination is a growing method that proliferates the number of applications of cloud computing by reusing attractive services. In this article, we survey the service composition mechanisms such as all clouds, base cloud, smart cloud, COM2 and clustering algorithms which have been used in the cloud environments. Service composition methods can be applied in two main environments: single and multicloud where due to the importance of multi-cloud environments (MCE), some existing and important methods are analyzed in the multi-cloud environment in this article. This paper also provides a discussion of existing methods in terms of running time and finding the optimal cloud composition. Obtained results showed the performance of each method in some terms.

A flexible QoS-aware Web service composition method by multi-objective optimization in cloud manufacturing

Cloud manufacturing, combining Web services via internet to a cooperative manufacturing system, has been an increasing popularity for global manufacturing. It will unlock the tremendous value in the massive amount of data being generated by the manufactories. The problem of QoS-aware Web service composition (QWSC), i.e., selecting appropriate service for each component of a service composition from a pool of functionally identicalservice to satisfy the users’ end-to-end QoS constraints, is a core of the cloud manufacturing. A novel QWSC method by multi-objective optimization is proposed to help users to make a flexible decision. First of all, the problem of QWSC is formulated to a multi-objective optimization model where either QoS performance or QoS risk (variance comparing to the user‘s QoS requirement) is the individual optimization objective. And then, an efficient ε-dominance multi-objective evolutionary algorithm (EDMOEA) is developed to solve the presented model. Finally, experimental results verify the effectiveness and efficiency of the proposed method for the large-scale QWSC problem.

A Service Composition using Hierarchical Model in Multiple Service Environment

Internet-of-Things (IoT) becomes one of the most promising future paradigms, which foresees enormous amounts of interoperable things and heterogeneous services. The goal of IoT is to enable all things connected and brings all kinds information and services to people. However, such a great deal of information may lead to cognitive overload or restrain in productivity of people. Thus, it is a necessity to build intelligent mechanisms to assist people in accessing the information or services they needed in a proactive manner. Most of previous related mechanisms are built on well-defined web services and lack of consideration of constrained resources. This paper suggests a services composition method by adapting a hierarchical model, which is a graph-based model composed of four layers: Context Layer, Event Layer, Service Layer and Device Layer. With a such multi-layer graph, service composition can be achieved by the iteration of layer by layer. Then, to evaluate the effectiveness of this proposed hierarchical model, a real-life emergency response dataset is applied and the experimental results are composed with the general probabilistic method and indicate that the proposed method is help for compositing multiple services while considering given context and constrained resources.

The case-library method for service composition and optimal selection of big manufacturing data in cloud manufacturing system

Service composition and optimal selection (SCOS) technology composes basic services to satisfy various users’ needs and realize enterprise resource efficient allocation and value maximization. Since sharply increasing scale of the cloud manufacturing (CMfg) resource pool, and the growing sophistication of user requests, will make composed service a sharp increase in the quantity, type, dimension and complexity, which cause a big data environment for CMfg’s application and realization, this paper analyzes the difficulties and solutions of SCOS of big data in the future, especially for optimal selection from large-scale composed service execute paths (CSEP), and proposes two phases SCOS method based on case library. Firstly, cases, similar with users’ request, are searched from case library. Secondly, the cases are used to initial the existing optimization algorithm to solve large-scale optimal selection problem. Moreover, case library structure, user’s service request structure, similarity comparison, and realization process are studied. Compared to the existing optimization algorithm method, the prototype system using case library in large-scale CMfg could obtain a better optimization result.

A New Formal Modeling Method for Web Service Composition

With the rapid development of e-commerce, the application of web service composition is becoming increasingly important in many areas. If we apply unchecked web service composition, then we can only discover problems afterwards, and we have to devote unnecessary energy to repair. Here, formal modeling serves to be a premise to check. This paper brings up a formal modeling method for web service composition based on BDL (behavioral description language). The structure of this essay is to analyze the description of BPEL process, and we can get the formal model from the BPEL-to-BDL mapping relation.

A Reliable Automatic Service Composition Method Based on Type Theory

Web Service composition has become a critical technology to react to the complex functional requirements put forward by business logic in e-commerce. How to guarantee the reliability of composite Web service is a hot topic through all the phase of lifecycle, including design, development and deployment. To this end, we are motivated to propose a Type Theory (TT) based method to automatic service composition. First of all, a service model in form of TT representation defines the atomic services declarations, semantic relation and composition request. Then, a set of TT rules that helps to prove the satisfiability of the user’s request is formally given. Later, several useful tools transforming the service declaration files to the propositions and extracting the composition result from the proof is discussed. To face the challenge of reliability, we show a hybrid model, which dynamically binds service with the corresponding strict proving, to ensure the reliability of our method during compositing services. At last, the proposed method is implemented and a complete case study is conducted to demonstrate the applicability and effectiveness of our techniques.

Manufacturing service composition method based on networked collaboration mode

“Cluster Supply Chains” (CSCs) are a new type of business mode, which can help small- and medium-sized enterprises (SMEs) to respond to the changing market demands effectively through networked collaboration. As a large number of manufacturers start to adopt the emerging services paradigm, various cross-enterprise business collaborations in CSC can be deemed as the manufacturing service composition (MSC). However, existing web service composition methods are not appropriate for satisfying the specific demands of manufacturing services composition. The reason lies in two aspects: (1) Current Quality of Service (QoS) model cannot consider the horizontal collaboration between manufacturing services, which may ignore a lot of potential candidate services; (2) current methods are not good at dealing with the rapid increase of candidate service set, which need to be improved further in the searching capability. To solve the above problems, this paper first proposes the new QoS evaluation model of manufacturing service composition according to the networked collaboration pattern in CSC; secondly, the Genetic-Artificial Bee Colony (G-ABC) algorithm is proposed to enhance the performance of searching for the optimal solution based on the available horizontal composite service; finally, the effectiveness of the above solutions is validated through simulation experiments and the results are satisfactory.