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

Authors: Krishna Kant Agrawal; Sujeet Kumar; Jitendra Kumar Seth; Abhishek Kumar Gupta; Sonia Lamba

Addresses: School of Computer Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India ' GL Bajaj Institute of Management, Greater Noida, Uttar Pradesh, India ' Department of Information Technology, KIET Group of Institutions, Ghaziabad, Uttar Pradesh, India ' Department of Information Technology, Jagannath International Management School, New Delhi, India ' Department of Computer Science, ABES Engineering College Ghaziabad, Uttar Pradesh, India

Abstract: 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.

Keywords: cloud computing; fog computing; smart homes; task scheduling; metaheuristic algorithms; deep learning.

DOI: 10.1504/IJCC.2025.145657

International Journal of Cloud Computing, 2025 Vol.14 No.1, pp.54 - 85

Received: 14 Jul 2024
Accepted: 20 Dec 2024
Published online: 11 Apr 2025 *

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