Autonomous and Adaptive Scheduling Framework for Next-Generation Fog Computing Systems

Fog computing has emerged as a promising paradigm for supporting latency-sensitive Internet of Things (IoT) applications by extending Cloud capabilities closer to data sources. However, efficient task scheduling in Fog environments remains challenging due to heterogeneous resources, dynamic tasks, and strict Quality-of-Service (QoS) requirements. This work proposes an Autonomous Adaptive Scheduling Framework (AASF) that leverages Deep Reinforcement Learning (DRL) to intelligently allocate tasks across distributed Fog nodes. The framework incorporates a multi-objective optimization model that simultaneously minimizes latency, energy consumption, execution cost, and reliability risks. A comprehensive system model and analytical performance metrics are developed to guide scheduling decisions. The proposed framework aims to enable self-optimizing Fog ecosystems capable of adaptive resource management and scalable IoT service delivery.