Service Composition Approach Research Articles

Different Test Cases for e-Governance using Web Services

SOA is an ideal architectural solution for resolving the adaptation, interoperability and dynamic requirements of information services in e-government applications. Construction and test Web Service solution for e-government is becoming more and more important for e-government adoption. Test cases play an important role in software applications and tracing of bugs in many software projects. In web services there are different techniques and software tools used for accessing the information however sometime, data is updated from different remote locations and give common information. There are different approaches for service composition, ranging from manual to semi-automatic to fully automatic. However it is quite complex when taking independently developed services and integrate them. In e-governance, there are many applications, which work concurrently and give different values to different persons at an instance, which can be traced improperly. In this paper we propose how a service is working properly which is having many services in e-governance applications. Web sites are of different servers and databases but they can integrate by using web services i.e. by using SOA. In SOA architecture a service can test to other service as service requester and what the services are tested as service provider. The trust of service testing is an important criteria while testing a service about its performance, efficiency and accurate.

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.

Adaptive and context-aware service composition for IoT-based smart cities

Smart Cities are advancing towards an instrumented, integrated, and intelligent living space, where Internet of Things (IoT), mobile technologies and next generation networks are expected to play a key role. In smart cities, numerous IoT-based services are likely to be available and a key challenge is to allow mobile users perform their daily tasks dynamically, by integrating the services available in their vicinity. Semantic Service Oriented Architectures (SSOA) abstract the environment’s services and their functionalities as Semantic Web Services (SWS). However, existing service composition approaches based on SSOA do not support dynamic reasoning on user tasks and service behaviours to deal with the heterogeneity of IoT domains. In this paper, we present an adaptive service composition framework that supports such dynamic reasoning. The framework is based on wEASEL, an abstract service model representing services and user tasks in terms of their signature, specification (i.e., context-aware pre-conditions, post-conditions and effects) and conversation (i.e., behaviour with related data-flow and context-flow constraints). To evaluate our composition framework, we develop a novel OWLS-TC4-based testbed by combining simple and composite services. The evaluation shows that our wEASEL-based system performs more accurate composition and allows end-users to discover and investigate more composition opportunities than other approaches.

A Process Mining Based Service Composition Approach for Mobile Information Systems

Due to the growing trend in applying big data and cloud computing technologies in information systems, it is becoming an important issue to handle the connection between large scale of data and the associated business processes in the Internet of Everything (IoE) environment. Service composition as a widely used phase in system development has some limits when the complexity of relationship among data increases. Considering the expanding scale and the variety of devices in mobile information systems, a process mining based service composition approach is proposed in this paper in order to improve the adaptiveness and efficiency of compositions. Firstly, a preprocessing is conducted to extract existing service execution information from server-side logs. Then process mining algorithms are applied to discover the overall event sequence with preprocessed data. After that, a scene-based service composition is applied to aggregate scene information and relocate services of the system. Finally, a case study that applied the work in mobile medical application proves that the approach is practical and valuable in improving service composition adaptiveness and efficiency.

Converged Network-Cloud Service Composition with End-to-End Performance Guarantee

The crucial role of networking in cloud computing calls for federated management of both computing and networking resources for end-to-end service provisioning. Application of the Service-Oriented Architecture (SOA) in both cloud computing and networking enables a convergence of network and cloud service provisioning. One of the key challenges to high performance converged network-cloud service provisioning lies in composition of network and cloud services with end-to-end performance guarantee. In this paper, we propose a QoS-aware service composition approach to tackling this challenging issue. We first present a system model for network-cloud service composition and formulate the service composition problem as a variant of Multi-Constrained Optimal Path (MCOP) problem. We then propose an approximation algorithm to solve the problem and give theoretical analysis on properties of the algorithm to show its effectiveness and efficiency for QoS-aware network-cloud service composition. Performance of the proposed algorithm is evaluated through extensive experiments and the obtained results indicate that the proposed method achieves better performance in service composition than the best current MCOP approaches.

A bitwise-based indexing and heuristic-driven on-the-fly approach for Web service composition and verification

During the last decade, software engineering community has witnessed the emerging of SOA architecture, where Web services play a crucial role. It prompts the concept of Web Service Composition (WSC). Even though interesting, this issue posed some remarkable challenges, one of which is constraint handling. To be more precise, one needs to ensure that the composite Web service fulfills, at the same time, many constraints, including functional constraints, Quality of Service (QoS), and the execution order of the component services, or temporal relations. Those constraints are of different natures, thus finding an efficient verification on all kinds of constraints during the composition process is by no means a trivial task. Backed by a solid foundation of temporal logic, model checking (MC) is a suitable approach to handle this issue. However, MC-based approach suffers from the infamous problem of state-space explosion, making it limited when applied to real-life situations. The work in this paper addresses the problem by proposing various approaches for handling the state-space exploration, including (i) an introduction of an LTS-based model known as LTS4WS, which can avoid generating full schema of Web service composition and allow on-the-fly verification on the state space; (ii) heuristics strategies to find the best potential composition, and (iii) a bitwise-based indexing mechanism for fast location of suitable Web services. All of those approaches are unified in a single tool, known as WSCOVER. As a result, a significant improvement of performance has been made, especially as compared with the other existing works in the same field.

Big Data-Driven Service Composition Using Parallel Clustered Particle Swarm Optimization in Mobile Environment

The proliferation of mobile computing and smartphone technologies has resulted in an increasing number and range of services from myriad service providers. These mobile service providers support numerous emerging services with differing quality metrics but similar functionality. Facilitating an automated service workflow requires fast selection and composition of services from the services pool. The mobile environment is ambient and dynamic in nature, requiring more efficient techniques to deliver the required service composition promptly to users. Selecting the optimum required services in a minimal time from the numerous sets of dynamic services is a challenge. This work addresses the challenge as an optimization problem. An algorithm is developed by combining particle swarm optimization and k-means clustering. It runs in parallel using MapReduce in the Hadoop platform. By using parallel processing, the optimum service composition is obtained in significantly less time than alternative algorithms. This is essential for handling large amounts of heterogeneous data and services from various sources in the mobile environment. The suitability of this proposed approach for big data-driven service composition is validated through modeling and simulation.

Automated and flexible composition based on abstract services for a better adaptation to user intentions

In recent years, the composition of loosely coupled services with the aim of satisfying the user intention is a widely followed research topic. The composition of services implies the ability to select, coordinate, interact, and interoperate existing services. This is considered as a complex task. This complexity is mainly due to the large number of available services and their heterogeneity as they are created by different organizations. This complexity is increased when services must be dynamically and automatically composed to meet requirements which are not satisfied by existing services. In fact, an approach for service composition must offer the potential to achieve flexible and adaptable applications, by selecting and combining services based of the request and the context of the user. In this perspective, different approaches have been developed for services composition. However, most of the existing composition approaches tend to be static and not flexible in the sense that they do not have the ability to adapt to user requirements.To overcome these challenges, we propose a composition approach in which the generation of the composition schema is performed at runtime through the use of abstract services provided at design time. The composition process that we propose takes as input a structure of user requirements materialized by a graph of intentions and enriches this graph to explicit the implicit relationships. The enriched graph is used to generate an initial composition schema by building the control flow and selecting the appropriate abstract services. The selection of these services is based on the semantic matching and the degree of semantic affinity between abstract services. Then, the final composition schema is generated using a refinement mechanism of abstract services using semantic matching techniques and taking into account user context and constraints.

A Data Volume Aware Ant Colony Optimization Approach for Geographical Knowledge Cloud Service Composition

Geographical knowledge cloud service is a typical online service that provides big spatial data analysis with the function of knowledge discovery or decision-making. The composition of geographical knowledge cloud service imposes stricter requirements for better overall QoS and execution efficiency of the service chain. In this paper, we present a data volume aware ant colony optimization approach called DVA-MOACO algorithm for geographical knowledge cloud service composition. Our algorithm utilizes a multi-index service quality evaluation model, and improves the transition probability while considering the data transfer cost and other QoS constraints simultaneously when ant finding path. Our algorithm could reach the Pareto near optimal solution rapidly with better QoS performance and lower data transfer cost from numerous candidate solutions.

QoS-aware service composition for cloud manufacturing based on the optimal construction of synergistic elementary service groups

Cloud manufacturing (CMfg) has drawn extensive attentions from industrial community and academia. Quality of service (QoS)-aware service composition is critical to the on-demand use of distributed manufacturing resources and capabilities in CMfg systems. However, most previous work plainly composed composite services by the approach of one-to-one mapping-based service composition (OOM-SC), which leads to drawbacks to both the overall QoS of composite services and the success rate of service composition. To circumvent this, an approach of synergistic elementary service group-based service composition (SESG-SC) is proposed in this paper. It releases the assumption of one-to-one mapping between elementary services and subtasks, allowing a free combination of multiple functionally equivalent elementary services into a synergistic elementary service group (SESG) to perform each subtask collectively, thereby bettering the overall QoS and achieving more acceptable success rate. To introduce an optimal construction of SESGs into the optimization model of QoS-aware service composition, three kinds of redundant structures within SESGs are discussed and the corresponding QoS evaluation formulas are also proposed. To deal with the increasing computing complexity of the optimization model, an algorithm named matrix-coded genetic algorithm with collaboratively evolutional populations (MCGA-CEP) is designed in the current study. The experimental results indicate that the proposed SESG-SC approach significantly outperforms the previous approaches, and the proposed MCGA-CEP is sound on performance-wise.

Thematic series on service composition for the future internet

During the last few years, there has been a growing interest around the development of new ideas envisioning the opportunities of combining Internet and Services. The “Future Internet” (FI) concept has been introduced and it is now central to several initiatives. Specifically, with respect to the Internet of Services, many research communities refer to the well-founded Service-Oriented Computing (SOC) as the reference paradigm for FI computing. SOC promotes the idea of assembling application services into a network of services that can be loosely coupled to create flexible, dynamic business processes and agile software systems, which span organizations and computing platforms. Service composition approaches will certainly have an important role in shaping the SOC within the vision of FI. Despite the great interest in Service Composition, no common foundations and principles have been established yet for the FI of Services. Developing FI applications encompasses a variety of aspects, ranging from modeling and analysis issues in the early phases of their development, to issues related to their implementation and run-time management. This thematic series brings together innovative contributions to the research and development of novel service composition approaches to assist the design, development, validation and execution of service-oriented applications for the FI.

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.

Flexible Construction of Executable Service Compositions from Reusable Semantic Knowledge

Most service composition approaches rely on top-down decomposition of a problem and AI-style planning to assemble service components into a meaningful whole, impeding reuse and flexibility. In this article, we propose an approach that starts from declarative knowledge about the semantics of individual service components and algorithmically constructs a full-blown service orchestration process that supports sequence, choice, and parallelism. The output of our algorithm can be mapped directly into a number of service orchestration languages such as OWL-S and BPEL. The approach consists of two steps. First, semantic links specifying data dependencies among the services are derived and organized in a flexible network. Second, based on a user request indicating the desired outcomes from the composition, an executable composition is constructed from the network that satisfies the dependencies. The approach is unique in producing complex compositions out of semantic links between services in a flexible way. It also allows reusing knowledge about semantic dependencies in the network to generate new compositions through new requests and modification of services at runtime. The approach has been implemented in a prototype that outperforms related composition prototypes in experiments.

Two-stage approach for reliable dynamic Web service composition

Web service composition is a key technology for creating value-added services by integrating available services. With the rapid development of Service Computing, Cloud Computing, Big Data, and the Internet of Things, mass services with the same functionalities and different quality of service (QoS) values are available on the Internet. Moreover, due to the uncertainties of services’ application environment, a service’s QoS is highly dynamic; these two factors cause reliable dynamic Web service composition to be a challenge. To address this issue, this paper proposes a two-stage approach for reliable dynamic Web service composition. In the first stage, the top K Web service composition schemes based on each service’s historical QoS values are selected with the proposed algorithm named Culture Genetic Algorithm (CGA). Then, component services in the top K schemes are filtered out and employed as the candidate services for dynamic service composition. This operation can greatly reduce the number of available services and filter out better candidate services for dynamic service composition. Next, the global QoS constraints are decomposed into local QoS constraints with the CGA algorithm, and the global optimal problem of service composition is transformed into a local optimal service selection problem; this conversion increases the flexibility of dynamic service composition and provides a chance to predict QoS values of services before service selection. In the second stage, before selecting the best service for each task during the running of the service composition workflow, QoS values of each candidate service are predicted based on the improved case-based reasoning, and the best service is selected according to the predicted QoS values. Through QoS prediction, the reliability of the composite Web service can be greatly enhanced. Finally, experimental results show that the proposed method is feasible and effective.

Dynamic Service Discovery, Composition and Reconfiguration in a Model Mapping Business Process to Web Services

In any organization where SOA has been implemented, all of the web services are registered in UDDI and users’ needs are served by using appropriate web services. So in this paper, we will try to discover a service from repository first that can provide the required output to the user. The process becomes difficult when a single service is not able to fulfill a user’s need and we need a combination of services to answer complex needs of users. In our paper, we will suggest a simpler approach for dynamic service composition using a graph based methodology. This will be a design time service composition. This approach uses the functional and non-functional parameters of the services to select the most suitable services for composition as per user’s need. This approach involves “service classification” on the basis of functional parameters, “service discovery” on the basis of user’s need and then “service composition” using the selected services on the basis of non-functional parameters like response time, cost, security and availability. Another challenge in SOA implementation is that, once the composition has performed, some services may become faulty at runtime and may stop the entire process of serving a user’s need. So, we will also describe a way of “dynamic service reconfiguration” in our approach that will enable us to identify and replace a faulty service that is violating the SLA or is not accessible anymore. This service reconfiguration is done without redoing or reconfiguring the entire composition. In the end, to simulate the proposed approach, we will represent a prototype application built on php 5.4 using My SQL database at backend.

A Checking Service Composition Approach based on Model Transformation

The service oriented architecture (SOA) approach constitutes an ideal solution for distributed application development. However, its success is conditioned by a solution to the problem of dynamic service composition. The formal-based tools are ones of the promising solutions to this problem, because of their mathematical-basis who can provide possibility of doing computation on services behavior in order to confirm the existence or not of some particular behaviours. In this paper, the authors present an approach based on model transformation (MT) for checking behavioural compatibility between services. Elementary services behaviours, modelled as OWFNet, an extenstion of Petri Nets, are automatically translated to a LOTOS specification. The translation is done by a two-fold transformation, model to model (M2M) and model to text (M2T). The resulting Lotos text specification is submitted to a LOTOS toolkit to check service composability.

Mobility-Aware Service Composition in Mobile Communities

The advances in mobile technologies enable mobile devices to perform tasks that are traditionally run by personal computers as well as provide services to the others. Mobile users can form a service sharing community within an area by using their mobile devices. This paper highlights several challenges involved in building such service compositions in mobile communities when both service requesters and providers are mobile. To deal with them, we first propose a mobile service provisioning architecture named a mobile service sharing community and then propose a service composition approach by utilizing the Krill-Herd algorithm. To evaluate the effectiveness and efficiency of our approach, we build a simulation tool. The experimental results demonstrate that our approach can obtain superior solutions as compared with current standard composition methods in mobile environments. It can yield near-optimal solutions and has a nearly linear complexity with respect to a problem size.

Fruit Fly Optimization Algorithm for Network-Aware Web Service Composition in the Cloud

Service Oriented Computing (SOC) provides a framework for the realization of loosely coupled service oriented applications. Web services are central to the concept of SOC. Currently, research into how web services can be composed to yield QoS optimal composite service has gathered significant attention. However, the number and spread of web services across the cloud data centers has increased, thereby increasing the impact of the network on composite service performance experienced by the user. Recently, QoS-based web service composition techniques focus on optimizing web service QoS attributes such as cost, response time, execution time, etc. In doing so, existing approaches do not separate QoS of the network from web service QoS during service composition. In this paper, we propose a network-aware service composition approach which separates QoS of the network from QoS of web services in the Cloud. Consequently, our approach searches for composite services that are not only QoS-optimal but also have optimal QoS of the network. Our approach consists of a network model which estimates the QoS of the network in the form of network latency between services on the cloud. It also consists of a service composition technique based on fruit fly optimization algorithm which leverages the network model to search for low latency compositions without compromising service QoS levels. The approach is discussed and the results of evaluation are presented. The results indicate that the proposed approach is competitive in finding QoS optimal and low latency solutions when compared to recent techniques.

An interval-based fuzzy ranking approach for QoS uncertainty-aware service composition

Web services are a major technique for building loosely coupled distributed systems. QoS-aware service composition concerns the selection of candidate services while maximizing the overall Quality of Service (QoS). However, these approaches fail to take the uncertainty of QoS into account. The resultant QoS of composite service observed by users is therefore unstable and subject to risks of failing composition by end users. In this paper, we propose a QoS Uncertainty-aware Web Service Composition approach based on a novel QoS interval model. It performs evaluation under a trade-off between profit and stability of QoS in a fuzzy way, and selects out composite service among the numerous solutions with an overall more stable QoS. A Multi-criteria Decision Analysis (MCDA) method and improved Genetic Algorithm is introduced in this paper. Based on real-world dataset QWS, simulations validate the correctness of the interval model and demonstrate the performance of composite framework. A comparative study with traditional tentative model is also discussed briefly.

Multi-objective Optimization Model with AHP Decision-making for Cloud Service Composition

Cloud services are required to be composed as a single service to fulfill the workflow applications. Service composition in Cloud raises new challenges caused by the diversity of users with different QoS requirements and vague preferences, as well as the development of cloud computing having geographically distributed characteristics. So the selection of the best service composition is a complex problem and it faces trade-off among various QoS criteria. In this paper, we propose a Cloud service composition approach based on evolutionary algorithms, i.e., NSGA-II and MOPSO. We utilize the combination of multi-objective evolutionary approaches and Decision-Making method (AHP) to solve Cloud service composition optimization problem. The weights generated from AHP are applied to the Crowding Distance calculations of the above two evolutionary algorithms. Our algorithm beats single-objective algorithms on the optimization ability. And compared with general multi-objective algorithms, it is able to precisely capture the users’ preferences. The results of the simulation also show that our approach can achieve a better scalability.