An architecture for autonomic web service process planning

Web service composition is a technology that has received considerable attention in the last number of years. Languages and tools to aid in the process of creating composite web services have been received specific attention. Web service composition is the process of linking single web services together in order to accomplish more complex tasks. One area of web service composition that has not received as much attention is the area of dynamic error handling and re-planning, enabling autonomic composition. Given a repository of service descriptions and a task to complete, it is possible for AI planners to automatically create a plan that will achieve this goal. If however a service in the plan is unavailable or erroneous the plan will fail. Motivated by this problem, this paper suggests autonomous re-planning as a means to overcome dynamic problems. Our solution involves automatically recovering from faults and creating a context-dependent alternate plan

Constraint integration and violation handling for BPEL processes

Autonomic, i.e. dynamic and fault-tolerant Web service composition is a requirement resulting from recent developments such as on-demand services. In the context of planning-based service composition, multi-agent planning and dynamic error handling are still unresolved problems. Recently, business rule and constraint management has been looked at for enterprise SOA to add business flexibility. This paper proposes a constraint integration and violation handling technique for dynamic service composition. Higher degrees of reliability and fault-tolerance, but also performance for autonomously composed WS-BPEL processes are the objectives

Analysis of Autonomic Service Oriented Architecture

— Service-Oriented Architecture (SOA) enables composition of large and complex computational units out of the available atomic services. However, implementation of SOA, for its dynamic nature, could bring about challenges in terms of service discovery, service interaction, and service composition. SOA may often need to dynamically re-configure and re-organize its topologies of interactions between the web services because of some unpredictable events, such as crashes or network problems, which will cause service unavailability. Complexity and dynamism of the current and future global network systems require service architecture that is capable of autonomously changing its structure and functionality to meet dynamic changes in the requirements and environment with little human intervention. In this paper, formal models of a proposed autonomic SOA framework are developed and analyzed using Petri Net. The results showed that SOA can be improved to cope with dynamic environment and services unavailability by incorporating case-based reasoning and autonomic computing paradigm to monitor and analyze events and service requests, then to plan and execute the appropriate actions using the knowledge stored in knowledge database. Keywords— Service Oriented Architecture, autonomic computing, case-based reasoning, formal model, Petri Ne

Cognitively-inspired Agent-based Service Composition for Mobile & Pervasive Computing

Automatic service composition in mobile and pervasive computing faces many challenges due to the complex and highly dynamic nature of the environment. Common approaches consider service composition as a decision problem whose solution is usually addressed from optimization perspectives which are not feasible in practice due to the intractability of the problem, limited computational resources of smart devices, service host's mobility, and time constraints to tailor composition plans. Thus, our main contribution is the development of a cognitively-inspired agent-based service composition model focused on bounded rationality rather than optimality, which allows the system to compensate for limited resources by selectively filtering out continuous streams of data. Our approach exhibits features such as distributedness, modularity, emergent global functionality, and robustness, which endow it with capabilities to perform decentralized service composition by orchestrating manifold service providers and conflicting goals from multiple users. The evaluation of our approach shows promising results when compared against state-of-the-art service composition models.Comment: This paper will appear on AIMS'19 (International Conference on Artificial Intelligence and Mobile Services) on June 2

ACHIEVING AUTONOMIC SERVICE ORIENTED ARCHITECTURE USING CASE BASED REASONING

Service-Oriented Architecture (SOA) enables composition of large and complex computational units out of the available atomic services. However, implementation of SOA, for its dynamic nature, could bring about challenges in terms of service discovery, service interaction, service composition, robustness, etc. In the near future, SOA will often need to dynamically re-configuring and re-organizing its topologies of interactions between the web services because of some unpredictable events, such as crashes or network problems, which will cause service unavailability. Complexity and dynamism of the current and future global network system require service architecture that is capable of autonomously changing its structure and functionality to meet dynamic changes in the requirements and environment with little human intervention. This then needs to motivate the research described throughout this thesis. In this thesis, the idea of introducing autonomy and adapting case-based reasoning into SOA in order to extend the intelligence and capability of SOA is contributed and elaborated. It is conducted by proposing architecture of an autonomic SOA framework based on case-based reasoning and the architectural considerations of autonomic computing paradigm. It is then followed by developing and analyzing formal models of the proposed architecture using Petri Net. The framework is also tested and analyzed through case studies, simulation, and prototype development. The case studies show feasibility to employing case-based reasoning and autonomic computing into SOA domain and the simulation results show believability that it would increase the intelligence, capability, usability and robustness of SOA. It was shown that SOA can be improved to cope with dynamic environment and services unavailability by incorporating case-based reasoning and autonomic computing paradigm to monitor and analyze events and service requests, then to plan and execute the appropriate actions using the knowledge stored in knowledge database

Distribution and Self-Adaptation of a Framework for Dynamic Adaptation of Services

International audienceThe dynamism and scale of the infrastructure of the Internet of Services bring new needs to build autonomous services. These services have to be able to self-adapt to the variation of the environment. Moreover, these adaptations may span across multiple services and thus have to be coordinated, without breaking their autonomy. To this end we describe in this paper the approach we have chosen for SAFDIS, a framework to make coordinated adaptations of services. In this presentation, a particular emphasis is made on the distribution of the framework and how it helps to coordinate distributed adaptation. Benefits from the self-adaptation of the framework itself are also presented

Achieving Autonomic Web Service Compositions with Models at Runtime

Over the last years, Web services have become increasingly popular. It is because they allow businesses to share data and business process (BP) logic through a programmatic interface across networks. In order to reach the full potential of Web services, they can be combined to achieve specifi c functionalities. Web services run in complex contexts where arising events may compromise the quality of the system (e.g. a sudden security attack). As a result, it is desirable to count on mechanisms to adapt Web service compositions (or simply called service compositions) according to problematic events in the context. Since critical systems may require prompt responses, manual adaptations are unfeasible in large and intricate service compositions. Thus, it is suitable to have autonomic mechanisms to guide their self-adaptation. One way to achieve this is by implementing variability constructs at the language level. However, this approach may become tedious, difficult to manage, and error-prone as the number of con figurations for the service composition grows. The goal of this thesis is to provide a model-driven framework to guide autonomic adjustments of context-aware service compositions. This framework spans over design time and runtime to face arising known and unknown context events (i.e., foreseen and unforeseen at design time) in the close and open worlds respectively. At design time, we propose a methodology for creating the models that guide autonomic changes. Since Service-Oriented Architecture (SOA) lacks support for systematic reuse of service operations, we represent service operations as Software Product Line (SPL) features in a variability model. As a result, our approach can support the construction of service composition families in mass production-environments. In order to reach optimum adaptations, the variability model and its possible con figurations are verifi ed at design time using Constraint Programming (CP). At runtime, when problematic events arise in the context, the variability model is leveraged for guiding autonomic changes of the service composition. The activation and deactivation of features in the variability model result in changes in a composition model that abstracts the underlying service composition. Changes in the variability model are refl ected into the service composition by adding or removing fragments of Business Process Execution Language (WS-BPEL) code, which are deployed at runtime. Model-driven strategies guide the safe migration of running service composition instances. Under the closed-world assumption, the possible context events are fully known at design time. These events will eventually trigger the dynamic adaptation of the service composition. Nevertheless, it is diffi cult to foresee all the possible situations arising in uncertain contexts where service compositions run. Therefore, we extend our framework to cover the dynamic evolution of service compositions to deal with unexpected events in the open world. If model adaptations cannot solve uncertainty, the supporting models self-evolve according to abstract tactics that preserve expected requirements.Alférez Salinas, GH. (2013). Achieving Autonomic Web Service Compositions with Models at Runtime [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/34672TESI

Flexible provisioning of Web service workflows

Web services promise to revolutionise the way computational resources and business processes are offered and invoked in open, distributed systems, such as the Internet. These services are described using machine-readable meta-data, which enables consumer applications to automatically discover and provision suitable services for their workflows at run-time. However, current approaches have typically assumed service descriptions are accurate and deterministic, and so have neglected to account for the fact that services in these open systems are inherently unreliable and uncertain. Specifically, network failures, software bugs and competition for services may regularly lead to execution delays or even service failures. To address this problem, the process of provisioning services needs to be performed in a more flexible manner than has so far been considered, in order to proactively deal with failures and to recover workflows that have partially failed. To this end, we devise and present a heuristic strategy that varies the provisioning of services according to their predicted performance. Using simulation, we then benchmark our algorithm and show that it leads to a 700% improvement in average utility, while successfully completing up to eight times as many workflows as approaches that do not consider service failures