Wuhui Chen

A Scalable Architecture for Automatic Service Composition

This paper addresses automatic service composition (ASC) as a means to create new value-added services dynamically and automatically from existing services in service-oriented architecture and cloud computing environments. Manually composing services for relatively static applications has been successful, but automatically composing services requires advances in the semantics of processes and an architectural framework that can capture all stages of an applications lifecycle. A framework for ASC involves four stages: planning an execution workflow, discovering services from a registry, selecting the best candidate services, and executing the selected services. This four-stage architecture is the most widely used to describe ASC, but it is still abstract and incomplete in terms of scalable goal composition, property transformation for seamless automatic composition, and integration architecture. We present a workflow orchestration to enable nested multilevel composition for achieving scalability. We add to the four-stage composition framework a transformation method for abstract composition properties. A general model for the composition architecture is described herein and a complete and detailed composition framework is introduced using our model. Our ASC architecture achieves improved seamlessness and scalability in the integrated framework. The ASC architecture is analyzed and evaluated to show its efficacy.

Improving efficiency of service discovery using Linked data-based service publication

It is considered that Web services have had a tremendous impact on the web as a potential silver bullet for supporting a distributed service-based economy on a global scale. However, despite the outstanding progress, their uptake on a web scale has been significantly less than initially anticipated due to higher usage thresholds. For instance, it is a hard task for service provider to seek appropriate semantic information such as OWL ontologies for service annotation in the service publication stage due to the fact that nowadays we are suffering from serious lack of available and ubiquitous ontologies for global consensus. Also it is not realistic for query users who do not possess much semantic knowledge to specify their requests with associated semantic information in the service discovery stage. In this paper, we propose a methodology to build a global social service network based on Link data principles for reducing the using thresholds. First, we propose Linked social service which is published on the open web by following Linked data principles with social link, and then we suggest a new platform for constructing a global social service network based on Linked social service. Then, an approach is proposed to enable exploitation of global social service network, providing Linked Social Service as a Service. Finally, experimental results show that the Linked social service can reduce the using threshold by enabling exploring service to service based on the global social service network.

Web-Service Clustering with a Hybrid of Ontology Learning and Information-Retrieval-Based Term Similarity

Organizing Web services into functionally similar clusters, is an efficient approach to discovering Web services efficiently. An important aspect of the clustering process is calculating the semantic similarity of Web services. Most current clustering approaches are based on similarity-distance measurement, including keyword, ontology and information-retrieval-based methods. Problems with these approaches include a shortage of high quality ontologies and a loss of semantic information. In addition, there has been little fine-grained improvement in existing approaches to service clustering. In this paper, we present a new approach to grouping Web services into functionally similar clusters by mining Web service documents and generating an ontology via hidden semantic patterns present within the complex terms used in service features to measure similarity. If calculating the similarity using the generated ontology fails, the similarity is calculated by using an information-retrieval-based term-similarity method that adopts term-similarity measuring techniques used by thesaurus and search engines. Another important aspect of high performance in clustering is identifying the most suitable cluster center. To improve the utility of clusters, we propose an approach to identifying the cluster center that combines service similarity with the term frequency-inverse document frequency values of service names. Experimental results show that our clustering approach performs better than existing approaches.

Web Service Clustering using a Hybrid Term-Similarity Measure with Ontology Learning

Clustering Web services into functionally similar clusters is a very efficient approach to service discovery. A principal issue for clustering is computing the semantic similarity between services. Current approaches use similarity-distance measurement methods such as keyword, information-retrieval or ontology based methods. These approaches have problems that include discovering semantic characteristics, loss of semantic information and a shortage of high-quality ontologies. In this paper, the authors present a method that first adopts ontology learning to generate ontologies via the hidden semantic patterns existing within complex terms. If calculating similarity using the generated ontology fails, it then applies an information-retrieval-based method. Another important issue is identifying the most suitable cluster representative. This paper proposes an approach to identifying the cluster center by combining service similarity with term frequency—inverse document frequency values of service names. Experimental results show that our term-similarity approach outperforms comparable existing approaches. They also demonstrate the positive effects of our cluster-center identification approach.

Toward Better Quality of Service Composition Based on a Global Social Service Network

Automatic service composition can create new value-added services dynamically and automatically from existing services in an envisioned service-oriented architecture. However, despite considerable progress, web-scale uptake has been significantly less than initially anticipated because of several challenging issues, such as poor scalability, exponentially expanding search time in large search spaces, and the lack of service sociability caused by the isolation of services. In this paper, we propose an innovative methodology for moving from isolated service islands to a global social service network (GSSN) by developing a network model that supports service sociability. First, we propose the construction of a GSSN based on the quality of social links. We then propose an algorithm that maps the GSSN into a service cluster network to reduce the search space, and a quality-driven composition approach that enables exploitation of the service cluster network by providing workflow as a service. Finally, experimental results show that our GSSN-based approach can solve the service composition problem well, improving not only the response time but also the success rate.

Clustering and Spherical Visualization of Web Services

Web service clustering is one of a very efficient approach to discover Web services efficiently. Current clustering approaches use traditional clustering algorithms such as agglomerative as the clustering algorithm. The algorithms have not provided visualization of service clusters that gives inspiration for a specific domain from visual feedback and failed to achieve higher noise isolation. Furthermore iterative steps of algorithms consider about the similarity of limited number of services such as similarity of cluster centers. This leads to reduce the cluster performance. In this paper we apply a spatial clustering technique called the Associated Keyword Space(ASKS) which is effective for noisy data and projected clustering result from a three-dimensional (3D) sphere to a two dimensional(2D) spherical surface for 2D visualization. One main issue, which affects to the performance of ASKS algorithm is creating the affinity matrix. We use semantic similarity values between services as the affinity values. Most of the current clustering approaches use similarity distance measurement such as keyword, ontology and information-retrieval-based methods. These approaches have problem of short of high quality ontology and loss of semantic information. In this paper, we calculate the service similarity by using hybrid term similarity method which uses ontology learning and information retrieval. Experimental results show our clustering approach is able to plot similar services into same area and aid to search Web services by visualization of the service data on a spherical surface.

Cost-Aware Streaming Workflow Allocation on Geo-Distributed Data Centers

The virtual machine (VM) allocation problem in cloud computing has been widely studied in recent years, and many algorithms have been proposed in the literature. Most of them have been successfully applied to batch processing models such as MapReduce; however, none of them can be applied to streaming workflow well because of the following weaknesses: 1) failure to capture the characteristics of tasks in streaming workflow for the short life cycle of data streams; 2) most algorithms are based on the assumptions that the price of VMs and traffic among data centers (DCs) are static and fixed. In this paper, we propose a streaming workflow allocation algorithm that takes into consideration the characteristics of streaming work and the price diversity among geo-distributed DCs, to further achieve the goal of cost minimization for streaming big data processing. First, we construct an extended streaming workflow graph (ESWG) based on the task semantics of streaming workflow and the price diversity of geo-distributed DCs, and the streaming workflow allocation problem is formulated into mixed integer linear programming based on the ESWG. Second, we propose two heuristic algorithms to reduce the computational space based on task combination and DC combination in order to meet the strict latency requirement. Finally, our experimental results demonstrate significant performance gains with lower total cost and execution time.

Big Data Infrastructure for Active Situation Awareness on Social Network Services

Awareness computing aims at our final goal in computer science to simulate humans awareness and cognition. Awareness of social network knowledge in everyday life is actively enabled by big data society. In this paper, we investigate infrastructure for big data analytics for social network services, and propose TF-IDF calculation on big data infrastructure to be aware of social relations on social networks.

Tology-Aware Optimal Data Placement Algorithm for Network Traffic Optimization

We propose a new optimal data placement technique to improve the performance of MapReduce in cloud data centers by considering not only the data locality but also the global data access costs. We first conducted an analytical and experimental study to identify the performance issues of MapReduce in data centers and to show that MapReduce tasks that are involved in unexpected remote data access have much greater communication costs and execution time, and can significantly deteriorate the overall performance. Next, we formulated the problem of optimal data placement and proposed a generative model to minimize global data access cost in data centers and showed that the optimal data placement problem is NP-hard. To solve the optimal data placement problem, we propose a topology-aware heuristic algorithm by first constructing a replica-balanced distribution tree for the abstract tree structure, and then building a replica-similarity distribution tree for detail tree construction, to construct an optimal replica distribution tree. The experimental results demonstrated that our optimal data placement approach can improve the performance of MapReduce with lower communication and computation costs by effectively minimizing global data access costs, more specifically reducing unexpected remote data access.

Context-Aware Filtering and Visualization of Web Service Clusters

Web service filtering is an efficient approach to address some big challenges in service computing, such as discovery, clustering and recommendation. The key operation of the filtering process is measuring the similarity of services. Several methods are used in current similarity calculation approaches such as string-based, corpus-based, knowledge-based and hybrid methods. These approaches do not consider domain-specific contexts in measuring similarity because they have failed to capture the semantic similarity of Web services in a given domain and this has affected their filtering performance. In this paper, we propose a context-aware similarity method that uses a support vector machine and a domain dataset from a context-specific search engine query. Our filtering approach uses a spherical associated keyword space algorithm that projects filtering results from a three-dimensional sphere to a two-dimensional (2D) spherical surface for 2D visualization. Experimental results show that our filtering approach works efficiently.