An Automated QoS Value Extraction Using Egret Swarm Optimization for Service Correlation Mapping

In the contemporary landscape of multi-service computing, optimizing Quality of Service (QoS) is paramount for ensuring both user satisfaction and system efficiency. Traditional methods of QoS optimization often fall short in dynamically adapting to the complexities of service-oriented architectures. This paper introduces a novel approach utilizing Egret Swarm Optimization (ESO) for automated extraction of QoS values and service correlation mapping. The proposed method leverages the unique characteristics of ESO to efficiently navigate the solution space, identifying optimal service compositions that enhance overall QoS. By integrating ESO with advanced correlation mapping techniques, the framework automatically correlates services to their respective QoS metrics, ensuring a more coherent and responsive service environment. Experimental results demonstrate the efficacy of the ESO-based approach in achieving superior QoS optimization compared to conventional algorithms. The findings indicate that this method not only improves performance metrics but also adapts to dynamic service demands, making it a robust solution for modern multi-service systems. This research provides a significant step forward in the field of automated QoS optimization, offering a scalable and effective tool for managing complex service interactions in real-time.