QoS-Aware Middleware for Web Services Composition

The paradigmatic shift from a Web of manual interactions to a Web of programmatic interactions driven by Web services is creating unprecedented opportunities for the formation of online Business-to-Business (B2B) collaborations. In particular, the creation of value-added services by composition of existing ones is gaining a significant momentum. Since many available Web services provide overlapping or identical functionality, albeit with different Quality of Service (QoS), a choice needs to be made to determine which services are to participate in a given composite service. This paper presents a middleware platform which addresses the issue of selecting Web services for the purpose of their composition in a way that maximizes user satisfaction expressed as utility functions over QoS attributes, while satisfying the constraints set by the user and by the structure of the composite service. Two selection approaches are described and compared: one based on local (task-level) selection of services and the other based on global allocation of tasks to services using integer programming

Quality Driven Web Services Composition

The process-driven composition of Web services is emerging as a promising approach to integrate business applications within and across organizational boundaries. In this approach, individual Web services are federated into composite Web services whose business logic is expressed as a process model. The tasks of this process model are essentially invocations to functionalities offered by the underlying component services. Usually, several component services are able to execute a given task, although with different levels of pricing and quality. In this paper, we advocate that the selection of component services should be carried out during the execution of a composite service, rather than at design-time. In addition, this selection should consider multiple criteria (e.g., price, duration, reliability), and it should take into account global constraints and preferences set by the user (e.g., budget constraints). Accordingly, the paper proposes a global planning approach to optimally select component services during the execution of a composite service. Service selection is formulated as an optimization problem which can be solved using efficient linear programming methods. Experimental results show that this global planning approach outperforms approaches in which the component services are selected individually for each task in a composite service

Task Memories & Task Forums : A Foundation for Sharing Service-based Personal Processes

International audienceThe growing number of online accessible services call for effective techniques to support users in discovering, selecting, and aggregating services. We present WS-Advisor, a framework for enabling users to capture and share task memories. A task memory represents knowledge (e.g., context and user rating) about services selection history for a given task. WS-Advisor provides a declarative language that allows users to share task definitions and task memories with other users and communities. The service selection component of this framework enables a user agent to improve its service selection recommendations by leveraging task memories of other user agents with which the user share tasks in addition to the local task memories

Task Memories & Task Forums: A Foundation for Sharing Service-based Personal Processes

Abstract. The growing number of online accessible services call for effective techniques to support users in discovering, selecting, and aggregating services. We present WS-Advisor, a framework for enabling users to capture and share task memories. A task memory represents knowledge (e.g., context and user rating) about services selection history for a given task. WS-Advisor provides a declarative language that allows users to share task definitions and task memories with other users and communities. The service selection component of this framework enables a user agent to improve its service selection recommendations by leveraging task memories of other user agents with which the user share tasks in addition to the local task memories.