The exponential growth of Internet of Things (IoT) devices has intensified the demand for efficient and responsive services. To address this demand, fog and edge computing have emerged as distributed paradigms that bring computational resources closer to end users, reducing latency, bandwidth limitations, and energy consumption. However, these paradigms present challenges in resource management due to resource constraints, computational heterogeneity, dynamic workloads, and diverse Quality of Service (QoS) requirements. This article presents a comprehensive survey of state-of-the-art resource management strategies in microservices-based fog and edge computing, focusing on energy-efficient solutions. We systematically review and classify over 136 studies (2020–2024) into five key subdomains: service placement, resource provisioning, task scheduling and offloading, resource allocation, and instance selection. Our categorization is based on optimization techniques, targeted objectives, and the strengths and limitations of each approach. Additionally, we examine existing surveys, identifying unresolved challenges and gaps in the literature. By highlighting the lack of synergy among fundamental resource management components, we outline promising research directions leveraging AI-driven optimization, quantum computing, and serverless computing. This survey serves as a comprehensive reference for researchers and practitioners, providing a unified, energy-aware perspective on resource management in microservices-based fog and edge computing, and paving the way for more integrated, efficient, and sustainable future solutions.
