A Comprehensive Survey on Resource Management for IoT Applications in Edge-Fog-Cloud Environments

Resource management in Edge-Fog-Cloud environments has emerged as a fundamental challenge in modern distributed computing systems, primarily due to the heterogeneous nature of underlying infrastructure, highly dynamic workloads, and the need to optimize multiple, often conflicting, Quality of Service (QoS) metrics such as latency, energy consumption, cost, and security. These environments are characterized by their layered architecture and real-time decision-making demands, which require efficient coordination among distributed resources to meet the expectations of both users and applications. Although several surveys in the literature have explored aspects of resource management, they tend to focus narrowly on specific components such as resource allocation, task scheduling, or workload placement, often neglecting the interdependence of these components. Moreover, a considerable number of surveys fail to consider the rapidly evolving landscape of edge and fog computing, where dynamic environmental changes and fluctuating user demands are becoming the norm. To bridge these gaps, this survey provides a comprehensive and up-to-date review of resource management strategies in Edge-Fog-Cloud ecosystems. We adopt a holistic perspective that integrates both combinatorial optimization techniques and learning-based methods, highlighting their roles in addressing different facets of resource management. The survey delves into five key aspects: task scheduling, resource placement, allocation, offloading, and service migration. We propose a structured taxonomy that classifies existing works based on their objectives, applied techniques, QoS decision-making mechanisms, and environmental assumptions. Special attention is given to the synergy between combinatorial and Machine Learning-based approaches, identifying where each excels and under which conditions their integration can yield more robust solutions. Ultimately, this survey outlines the current limitations of the field, uncovers open challenges, and suggests future research directions to enable more intelligent, efficient, and adaptive resource management solutions.