A systematic review of QoS enhancement techniques in microservices architecture

Abstract

Microservices architecture (MSA) has emerged as a dominant paradigm for developing scalable, flexible, and independently deployable applications. This approach improves the maintainability and scalability of applications by decomposing them into smaller and self-contained services. However, ensuring high Quality of Service (QoS) in dynamic environments remains a persistent challenge due to the distributed nature, complexity, and continuously changing user requirements. The main goal of this study is to thoroughly examine the key quality attributes that greatly affect the QoS of microservices applications, including performance, reliability, maintainability, and fault tolerance. It also provides deep insights into how machine learning (ML) can be used to improve these quality attributes. Additionally, this survey identifies major challenges and limitations in enhancing the QoS of microservices and explores potential future research directions. It emphasizes the need to focus on quality attributes like security, testability, fault prediction, and reliability. The study also suggests investigating alternative deployment environments, such as edge and fog computing, to address related challenges. These advancements will contribute to more robust, reliable, and user-friendly microservices applications, ultimately better meeting the evolving demands of users and businesses.