Authors: Samantha Green, Richard Morgan, Katherine Lewis, Benjamin Scott, Chaitanya Srinivas, Akhilesh Achari
Abstract: Modern cloud-native applications increasingly rely on microservice architectures to achieve scalability, flexibility, and resilience. However, the growing complexity of distributed environments presents significant challenges in performance management, resource allocation, fault detection, and service coordination. This paper proposes an intelligent self-optimizing microservice framework driven by autonomous feedback loops that continuously monitor, analyze, and adapt system behavior in real time. The framework integrates feedback-driven control models, artificial intelligence techniques, and automated decision-making mechanisms to dynamically optimize service performance, resource utilization, and operational reliability. By leveraging continuous feedback from runtime metrics, system events, and workload patterns, the proposed approach enables proactive adaptation to changing environmental conditions and application demands without human intervention. The study investigates key architectural components, optimization strategies, and autonomous control mechanisms that support self-healing, self-scaling, and self-configuring capabilities within microservice ecosystems. Experimental analysis demonstrates notable improvements in response time, throughput, fault tolerance, and infrastructure efficiency when compared with conventional static management approaches. The results indicate that autonomous feedback-driven optimization provides a robust foundation for developing intelligent, adaptive, and resilient microservice-based systems capable of meeting the demands of modern cloud and edge computing environments.
DOI: http://doi.org/10.5281/zenodo.20797329
