Service Composition Process Research Articles

Modelling and exploring historical records to facilitate service composition

Along with a continuously growing number of Web services, how to locate appropriate Web services to complete the task of service composition is becoming more critical. Differing from most recent studies which mainly focus on functional and non-functional properties, we mine nuggets from the Historical Service-composition Dataset HSD, which carries related users' past experiences. In this paper, a graph mining based recommendation approach is presented to facilitate the process of service composition. In particular, we first extend the graph mining approach gSpan to recognise Frequently Used Web Services FUWS with their connecting structures from HSD. Then, according to the records in HSD, which share the same FUWSs with user's partially composed service, a bunch of Web services with higher probability is recommended automatically as candidates. Finally, the skyline approach is adopted for optimal composite service selection with consideration of overall quality of services QoS. Furthermore, experiments based on 1,530 real Web services demonstrate the effectiveness and efficiency of our approach.

Verifying the Concurrent Properties in BPEL Based Web Service Composition Process

The relatively new web service software paradigm involves services which are loosely coupled, highly reusable and flexible. By specifying the workflow of individual services, Web service composition enhances the ability to handle more complex business processes and provides many value-added services. In this article, we propose an extended control flow graph (XCFG) to formally model the workflow of Web service composition specified in BPEL, and corresponding techniques to verify concurrent properties, such as deadlock-free, non-conflict, and link non-redundant. XCFG can model not only the workflow of BPEL but also the synchronization control dependencies among concurrent activities. Meanwhile, each element of XCFG keeps record of related information of corresponding activity in BPEL so as to support further analysis and verification. Experimental study validates the effectiveness and efficiency of the proposed XCFG-based technique.

Robust Dynamic Service Composition in Sensor Networks

Service modeling and service composition are software architecture paradigms that have been used extensively in web services where there is an abundance of resources. They mainly capture the idea that advanced functionality can be realized by combining a set of primitive services provided by the system. Many efforts in web services domain focused on detecting the initial composition, which is then followed for the rest of service operation. In sensor networks, however, communication among nodes is error-prone and unreliable, while sensor nodes have constrained resources. This dynamic environment requires a continuous adaptation of the composition of a complex service. In this paper, we first propose a graph-based formulation for modeling sensor services that maps to the operational model of sensor networks and is amenable to analysis. Based on this model, we formulate the process of sensor service composition as a cost-optimization problem and show that it is NP-complete. Two heuristic methods are proposed to solve the composition problem: the top-down and the bottom-up approaches. We discuss centralized and distributed implementations of these methods. Finally, using ns-2 simulations, we evaluate the performance and overhead of our proposed methods.

A Bounded Model Checking Approach for the Verification of Web Services Composition

In this paper, we propose a bounded model-checking based approach for the verification of declarative Web services composition processes using satisfiability solving (SAT). The need for the bounded model-checking approach stems from the nature of declarative processes as they are defined by only specifying the constraints that mark the boundary of the solution to the composition process. The proposed approach relies on using Event Calculus (EC) as the modeling formalism with a sound and complete EC to SAT encoding process. The use of EC as the modeling also formalism allows for a highly expressive approach for both the specification of composition model and for the specification of verification properties. Furthermore, as the conflict clauses returned by the SAT solver can be significantly large for complex processes and verification requirements, we propose a filtering criterion and defined patterns for identifying the clauses of interest for process verification.

Web service dynamic composition based on decomposition of global QoS constraints

Virtual enterprise is a temporary network of independent companies or enterprises that can quickly bring together to fulfill a value-added task. With the development of Web service technologies, the enterprise business systems can be encapsulated as Web services. Establishing a virtual enterprise is actually a process of Web service composition. As more and more Web services with the same functionalities and different Quality of Service (QoS) are available, QoS-aware Web service dynamic composition becomes an active research issue. Although several solutions have been provided for this issue, most of these methods based on global optimization, their poor performance render them inappropriate for applications with dynamic and real-time requirements. Moreover, these methods do not consider the commercial agreements and historical contact information between Web services with combination relationship. In this paper, we propose an approach for Web service dynamic composition based on global QoS constraints decomposition. The proposed method consists of three steps: Firstly, global QoS constraints are decomposed into local constraints optimally by a new algorithm named Culture Genetic Algorithm. Secondly, QoS values determination rules are designed to determine QoS values of candidate services. Thirdly, best Web services that can satisfy the local constraints are selected for each task during the running time. Experimental results show that our approach has better performance in solving the problem of QoS-aware Web service dynamic composition.

A correlation-driven optimal service selection approach for virtual enterprise establishment

A virtual enterprise is an emerging business cooperation model which allows rapid response to the unpredictable market behavior and opportunity. For service oriented enterprises, where computing resources are encapsulated as services and published online, establishing a virtual enterprise can be regarded as a process of service composition. As there are increasing numbers of available services providing similar functionalities but with different quality values, and with potential business correlations among them, it is not trivial to orchestrate a composite service with optimal overall quality of service (QoS). In this paper, we formally propose a business correlation model including both quality correlations and selection correlations, and then present an efficient approach for correlation-driven QoS-aware optimal service selection based on a genetic algorithm. The genetic algorithm is tailored with niching technology, a repair operator and a penalty mechanism. The effectiveness and efficiency of the approach are demonstrated via empirical studies at last.

Service discovery platform for ubiquitous environments

The capability to easily fi nd useful services in ubiquitous environments becomes increasingly critical in several application domains. Current approaches for ubiquitous services retrieval are mostly limited to the matching of their inputs/outputs possibly enhanced with some ontological knowledge. Recent research has demonstrated that this approach is not suffi cient to discover relevant services. Motivated by these concerns, this work proposes a service discovery platform in ubiquitous environments based on behavior matchmaking. We argue that in many situations the service composition process requires a service discovery phase based on matchmaking of behavior specifi cations of the components. Consequently, even if a service that meets exactly the user requirements does not exist, the most similar ones will be retrieved.

Ontological Description and MAS based Composition Process of SWS's for Recruitment Domain

that information is spread over internet and to retrieve useful information easily by mean of intelligent computing is the growing field of research. This need gives birth to semantic web services and various approaches are given for composition of these services. The key problem is to fulfill user's request. Intelligent Agents are used as a powerful approach for semantic web services selection and composition. Agent can be viewed as a self contained autonomous software component or say codes which is used for fulfillment of users request in any domain. Here in this paper semantic web services and agent capability is used for Man-Power Recruitment System (MPRS). Ontology of domain is used as a complete domain knowledge. Tools like Protege is used to create agent profile which shows the ontological description of recruitment domain. This paper focuses on how multi-agent system provides a new variety for recruitment system. This paper also spotlights to show an interactive recruiter for Man-Power Recruitment System (MPRS). Here service selection, task decomposition and service composition process of semantic web is exposed on the basis cognitive parameters, predilection and situation for man-power recruitment system.

Selecting skyline services for QoS-aware composition by upgrading MapReduce paradigm

With the development of web technologies and cloud computing, more and more services which provide similar functionality but differ in QoS are deployed on the Internet via cloud platforms. Recently, skyline analysis is adopted to select candidate services with better QoS to facilitate the process of QoS-aware service composition. However, the fast increasing number of services, multiple QoS attributes to be considered, and dynamic service environment pose a big challenge to skyline service selection. In this paper, we present a parallel skyline service selection method to improve the efficiency by upgrading the MapReduce paradigm. An angle-based dataspace partitioning approach is employed in our MapReduce based skyline service selection. In particular, we explore the dominance power of local skyline services to improve the efficiency of selection, and present two detailed algorithms. To handle the dynamic nature of service environment, we employ Paper-Tape (PT) model which is used to rapidly locate varying services, and present a dynamic skyline service selection algorithm based on PT model. By experimenting over both real and synthetical datasets, we demonstrate the efficiency of our proposed methods.

An Approach to Composite QoS Parameter based Web Service Selection

In any enterprise application Quality of Service (QoS) is an important attribute for selecting a service during the service composition process. Although availability and reliability have been considered as the predominant factors for estimating reputation, two aspects are lacking in the literature. First, their use is limited to composite service level and does not count at the atomic level. Second, their combined effect is not evaluated. Hence, the methodology of estimating reputation and its use for atomic service will have compounding effect on the overall quality of the composite service. We feel, better estimation of QoS can be done with both factors considered together on the simple premise that availability tells about only the probability of that service being up/running, but doesnot tell about its failure trend. In our work we present mathematical modelling of these predominant QoS factors using Markov model and Weibull analysis. A scenario has been simulated using Colored Petri Nets (CPN) to study the behavioral aspects. The outcome of our research is two fold. First, counting on probability of a service being up/running and its failure trend, together, results in a better estimation of its behavior and helps selecting the most appropriate one. Second, this resulted in selection of a service with higher reputation but lower usage cost, as opposed to using a single factor that resulted in higher reputation with higher cost.

Learning service recommendations

The as a service paradigm reflects the fundamental idea of providing basic coherent functionality in terms of components that can be utilised on demand. These so-called services may also be interconnected in order to provide more complex functionality. Automation of this service composition process is indeed a formidable challenge. In our work, we are addressing this challenge by decomposing service composition into sequential decision making steps. Each step is supported by a recommendation mechanism. If composition requests recur over time and if evaluations of composition results are fed back, a proper recommendation strategy can evolve over time through learning from experience. In this paper, we describe our approach of modelling this service composition and recommendation process as Markov decision process and of solving it by means of reinforcement learning. A case study serves as proof of concept.

Optimising the semantic web service composition process using bio-inspired methods

This paper presents two bio-inspired methods (one inspired by the cuckoo’s breeding behaviour, and another one inspired by natural evolution and genetics) for selecting the optimal or near-optimal solution in web service composition. The proposed methods are applied on an enhanced planning graph structure which models the composition search space for a given user request. The cuckoo-inspired selection method applies a 1-OPT heuristic to expand the search space in a controlled way such that the stagnation in a local optimum solution is avoided. The genetic-based selection method uses two memory structures to avoid the stagnation in a local optimum solution on one hand, and to ensure that exploitation and exploration are properly performed. The quality of a composition solution is evaluated in terms of QoS attributes and semantic quality. To validate the proposed methods we have implemented an experimental prototype and carried out experiments on a set of scenarios with different complexities. Finally, we comparatively analyse the experimental results obtained by applying the two selection methods.

Towards Integration of Knowledge Extraction from Process Interoperability in Future Internet Enterprise Systems

Current R&D directions sustained by the European Commission, by FP7 and in the near future by FP8 research programs, are focusing on the development and standardization of new technologies to sustain the “Future Internet”. In this context the development of new Internet related concepts and technologies oriented towards providing positive benefits for economy has been included in a broad concept of “Future Internet Based Enterprise Systems”. The Intelligent Manufacturing Systems (IMS) paradigms, are leading our Information Society towards New Economy-driven Knowledge Society copying with Global e-Markets new list of System of Systems requirements. Efficient interoperability, both horizontal and vertical, is a basic requirement towards the aim of Enterprise System Integration within Collaborative Concurrent Competitive Enterprises. In this context, the authors will propose a framework for web service composition and knowledge extraction form process execution based on process description embedded in Internet of Things compliant intelligent objects.

一种高效的服务组合优化算法

With the rapid increasing number of the independently developed web services that provide the same functionality but differ in quality of service (QoS), how to select corresponding component services for each task of a specific service composition process in the way that maximizes the aggregation QoS value, while satisfying the constraints set by the end-users is practice known as an NP hard problem. Current solutions are not suitable for most real-time decision-making service composition applications which are required to obtain an optimal result in reasonable time. In this paper, we present an approach which addresses the issue of this problem by constructing convex hulls. The proposed heuristic algorithm consists of two steps: first, convex hulls are constructed for reducing the search space, then the multi-objective optimization problem is solved based on M-HEU. The results of the experiments demonstrate that our approach can obtain a close-to-optimal solution in a reasonable amount of time.

Service Selection Based On Behavior Matching

Service selection is a key step in the service composition process. The selection mainly depends on the search function of UDDI. However, UDDI provides poor search facilities: it allows only a keyword based search of web services. Such kind of the keyword search method can not capture the underlying semantics of web services, and thus can not protect us from obtaining bad services since UDDI registry contains checked and unchecked services. In this paper, based on the formalized Service Component Architecture (SCA) model in our previous work and the enhanced service publishing framework by OASIS, we present behavior based service selection technique. By plugging our selection algorithm to IBM WebSphere Process Server, which is based on the SCA, the web services binding can be automatically completed in the running time. BPEL has been used to describe service composition.

Continuous-time MAXQ Algorithm for Web Service Composition

Web services composition present a technology to compose complex service applications from individual (atomic) services, that is, through web services composition, distributed applications and enterprise business processes can be integrated by individual service components developed independently. In this paper, we concentrate on the optimization problems of dynamic web service composition, and our goal is to find an optimal composite policy. Different from many traditional composite methods that do not scale to large continuous-time processes, we introduce a hierarchical reinforcement learning technique, i.e., a continuous-time unified MAXQ algorithm, to solve large-scale web service composition problems in the context of continuous-time semi-Markov decision process (SMDP) model under either average- or discounted-cost criteria. The proposed algorithm can avoid the “curse of modeling” and the “curse of dimensionality” existing in the optimization process. Finally, we use a travel reservation as an example to illustrate the high effectiveness of the proposed algorithm, and the simulation results show that, it has better optimization performance and faster learning speed than the flat Q-learning.

A QoS-aware composition method supporting cross-platform service invocation in cloud environment

With the increasing popularity of cloud computing technologies, more and more service composition processes are enacted and executed in could environment. Compared with the various and approximately infinite application requirements from end users, the web services held by a cloud platform are usually limited. Therefore, it is often a challenging effort to develop a service composition, in such a situation that only part of the functional qualified candidate services could be found inside a cloud platform. In this situation, the absent services will be invocated in a cross-platform way outside the cloud platform. In view of this challenge, a QoS-aware composition method is investigated for supporting cross-platform service invocation in cloud environment. Furthermore, some experiments are deployed to evaluate the method presented in this paper.

A quality of service (QoS)-aware execution plan selection approach for a service composition process

The quality of service (QoS)-aware service composition (QSC) problem is to find an execution plan of a service composition process which can ensure that the quality of service meets given user requirements. This paper proposes a branch and bound for execution plan selection (BB4EPS) algorithm to solve the QSC problem. In the proposed BB4EPS algorithm, a universal QoS model is used to evaluate the QoS parameters for service composition, and the QSC problem is modeled as an extended flexible constraint satisfaction framework, in which the utility function is applied to build the objective function. Numerical simulation results show that the proposed algorithm is feasible and effective.

FuDoCS

Modern enterprises are increasingly moving towards a service oriented architecture for data sharing by putting their data sources behind services, thereby providing an interoperable way to interact with their data. This class of services is known as DaaS ( Data-as-a-Service ) services. DaaS Composition is a powerful solution to answer the user's complex queries by combining primitive DaaS services. User preferences are a key aspect that must be considered in the service composition process. A more general and suitable approach to model preferences is based on fuzzy sets theory [3]. Fuzzy sets are very well suited to the interpretation of linguistic terms and constitute a convenient way for a user to express her/his preferences. For example, when expressing preferences about the price of a car, users often employ fuzzy terms like rather cheap, affordable , etc. However as DaaS services proliferate, a large number of candidate compositions that would use different (most likely competing) services may be used to answer the same query. Hence, it is important to set up an effective service composition framework that would identify and retrieve the most relevant services and return the top- k compositions according to the user preferences.

Compose Real-world Service With Context

Advances in the areas of smart embedded devices, computing and networking are leading to the emergence of the Internet of Things (IoT) composed of millions of heterogeneous devices. These devices will be interconnected, providing and consuming information available on the network and cooperate. As the improvement of the performance of industrial processes, the service-oriented approach can be adopted for smart embedded devices. Each device can offer its functionality as services. In such infrastructures, traditional web service compositions can not adapt to the dynamic environment for the Internet of Things. In this paper, we propose an approach to compose services with context. It can not only adapt to dynamic environment for the Internet of Things, but also satisfy the user’s preferences. We first build context ontology to describe the scenario for user. According to context information, the service composition process is presented.