Enhancing resource management in fog-cloud internet of things systems with deep learning-based task allocation

Abstract

A fog-cloud internet of things (IoT) system integrates fog computing with cloud infrastructure to efficiently manage processing data closer to the source, reducing latency and bandwidth usage. Efficient task scheduling in fog-cloud system is crucial for optimizing resource utilization and minimizing energy consumption. Even though many authors proposed energy efficient algorithms, failed to provide efficient method to decide the task placement between fog nodes and cloud nodes. The proposed hybrid approach is used to distinguish the task placement between fog and cloud nodes. The hybrid approach comprises the parametric task categorization algorithm (PTCA) for task categorization and the multi metric forecasting model (MMFM) based on deep deterministic policy gradient (DDPG) recurrent neural networks for scheduling decisions. PTCA classifies tasks based on priority, quality of service (QoS) demands, and computational needs, facilitating informed decisions on task execution locations. MMFM enhances scheduling by optimizing energy efficiency and task completion time. The experimental evaluation outperforms the existing models, including random forest (RF), support vector machine (SVM), and k-nearest neighbors (KNN). The proposed result shows an accuracy rate of 89%, and energy is consumed 50% lesser than the existing models. The proposed research advances energy-efficient task scheduling, enabling intelligent resource management in fog-cloud IoT environments.