An effective algorithm for predicting load and dynamic task scheduling in cloud fog architecture for smart homes

The need for smart homes with many devices and services continues to rise quickly. With this surge, smart homes need task scheduling and load-prediction algorithms to provide the proper services for the residents. A deep learning-based dynamic job scheduling and load prediction technique for cloud-fog smart homes is proposed in this paper. This algorithm forecasts task arrival rates at each fog node and assigns them to available fog nodes. It dynamically schedules tasks based on fog node workload. Another option is to send non-real-time jobs to the cloud and real-time tasks to the fog layer. This optimises load distribution for performance. Using these task assignee models and features, the program optimises prioritised tasks, scores, network latency, and device resource characteristics. We simulate the algorithm's performance in various workloads in this part. The proposed algorithms achieved in higher percentile for 93.79% latency, 95.00% throughput, 95.34% response time, 96.28% scalability, 94.20% fault-tolerance, 97.41% scheduling capacity, 91.41% load balancing capacity, and 95.22% priority management. The results indicate that such an algorithm significantly surpasses the conventional task scheduling methods in load balancing and shortens the average task response time.