PROJECT TITLE :

Requirements-Driven Self-Optimization of Composite Services Using Feedback Control

ABSTRACT:

In an uncertain and changing setting, a composite service wants to continuously optimize its business process and repair choice through runtime adaptation. To realize the overall satisfaction of stakeholder necessities, quality tradeoffs are required to adapt the composite service in response to the changing environments. Existing approaches on service selection and composition, but, are mostly based mostly on quality preferences and business processes selections created statically at the planning time. In this paper, we tend to propose a needs-driven self-optimization approach for composite services. It measures the quality of services (QoS), estimates the earned business worth, and tunes the preference ranks through a feedback loop. The detection of sudden earned business value triggers the proposed self-optimization method systematically. At the process level, a preference-based reasoner configures a requirements goal model in step with the tuned preference ranks of QoS necessities, reconfiguring the business process per its mappings from the goal configurations. At the service level, choice selections are optimized by utilizing the tuned weights of QoS criteria. We used an experimental study to evaluate the proposed approach. Results indicate that the new approach outperforms both mounted-weighted and floating-weighted service selection approaches with respect to earned business price and adaptation flexibility.