Survey on auto elasticity techniques for microservices in cloud computing

Assistant Professor, ISE Department, AIT Research Scholar, CSE Dept, JSSATE Bangalore, India
HOD, CSE Dept, JSSATE Bangalore, India

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Abstract

Microservices have become a popular way to build scalable and flexible cloud-native applications. However, the rapid growth of cloud services, edge computing platforms, and container-based deployments has created big challenges in managing computing resources efficiently. The distributed and loosely connected nature of microservices makes it hard to choose the best service placement, allocate resources dynamically, and scale automatically while keeping performance and service level agreements (SLAs) intact. Recent research has looked into smart resource management methods that use artificial intelligence (AI), machine learning (ML), and meta-heuristic optimization techniques to support self-managing and adaptable system behavior. These methods aim to improve quality of service (QoS), lower operational costs and energy use, and allow flexible resource provisioning in cloud-edge environments. This paper offers a thorough survey of smart and adaptable resource management strategies for microservice-based cloud and edge systems. The study reviews current solutions related to auto-scaling, service placement and migration, predictive container orchestration, and multi-objective optimization frameworks based on the Monitor-Analyze-Plan-Execute (MAPE) model. Additionally, the paper points out existing research challenges and discusses potential paths for creating fully self-managing cloud-native resource management systems.