Improving QoS of Microservices Architecture Using Machine Learning Techniques

Microservices architecture has gained significant popularity in the development of modern software applications due to its scalability, flexibility, and modularity. However, ensuring high-quality service delivery while maintaining the agility and responsiveness of microservices poses several challenges. This paper introduces an innovative method aimed at enhancing the Quality of Service (QoS) in microservices architecture-driven applications through the utilization of machine learning techniques. Initially, the primary factors contributing to the overall quality of microservices applications are identified. Subsequently, a machine learning-based framework is proposed for enhancing the QoS of such applications. To validate this framework, experimental assessments are conducted using sample microservices applications as case studies. The outcomes of these experiments demonstrate a significant enhancement in the overall QoS of the microservices application facilitated by the proposed framework.