A Systematic Review of Artificial Intelligence ( AI )‐Based Task Scheduling Techniques for Cloud‐Fog Computing Environments
Abstract
ABSTRACT The rapid proliferation of latency‐sensitive and data‐intensive applications has accelerated the adoption of cloud–fog computing architectures to support real‐time processing and distributed resource management. However, efficient task scheduling in cloud–fog environments remains a challenging problem because of resource heterogeneity, dynamic workloads, network variability, and stringent quality of service (QoS) requirements. Artificial intelligence (AI)‐based techniques, including machine learning (ML), deep learning (DL), deep reinforcement learning (DRL), and hybrid AI approaches, have emerged as promising solutions for addressing these challenges. This paper presents a systematic review of AI‐based task scheduling techniques in cloud–fog computing environments published between 2021 and 2025. Following a PRISMA‐inspired methodology, 74 high‐quality studies were selected and analyzed. The reviewed works are classified into ML, DL, DRL, and hybrid AI‐based scheduling categories and evaluated with respect to scheduling objectives, QoS parameters, datasets, evaluation tools, and implementation environments. The analysis reveals that AI‐driven scheduling approaches significantly improve key performance metrics, including task scheduling performance (14%–90%), latency reduction (15%–65%), energy optimization (15%–41%), load balancing (20%–45%), execution time reduction (15%–45%), and cost optimization (10%–32%). This review further highlights the increasing adoption of DL, DRL, and hybrid AI techniques for managing complex and dynamic cloud–fog infrastructures. Additionally, this study identifies critical research challenges, including scalability, resource heterogeneity, lack of standardized benchmark datasets, limited real‐world deployments, and insufficient consideration of network‐layer dynamics. Finally, emerging research directions and future opportunities for developing scalable, adaptive, and intelligent scheduling frameworks in cloud–fog computing environments are discussed. This article is categorized under: Technologies > Cloud Computing Technologies > Internet of Things Technologies > Artificial Intelligence
