Cloud-Native Software Platforms: A Survey of Intelligent Resource Management and Reliability

Abstract

Cloud-native software platforms  have  revolutionized modern application development by leveraging microservices, containerization, Kubernetes orchestration, Continuous Integration/Continuous Deployment (CI/CD), serverless computing, and Infrastructure as Code (IaC). These technologies enable scalable, resilient, and efficient application deployment while supporting dynamic resource management across distributed cloud environments. However, the increasing complexity of cloud-native systems introduces challenges related to resource allocation, workload scheduling, reliability, fault tolerance, observability, and operational efficiency. This survey presents a comprehensive review of intelligent resource management and reliability techniques in cloud-native software platformsIt examines cloud-native architectures, resource allocation, monitoring, scheduling, auto-scaling, load balancing, AI/ML-based resource optimization, and performance enhancement strategies. The survey also reviews reliability mechanisms, including fault tolerance, high availability, self healing, failure recovery, observability, and performance evaluation. Furthermore, it provides a comparative analysis of recent studies, identifies existing research challenges, and discusses future research directions toward developing intelligent, autonomous, scalable, secure, and highly reliable cloud-native software platforms.