Title: Deep learning-based task scheduling in edge computing

Authors: Bantupalli Nagalakshmi; Sumathy Subramanian

Addresses: School of Computer Science and Engineering, Vellore Institute of Technology, Vellore-632014, Tamil Nadu, India ' School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore-632014, Tamil Nadu, India

Abstract: A potential paradigm called edge computing (EC) has recently come to light that supports internet of things (IoT) applications that are resource allocation with low latency services at the network edge. For scheduling the application tasks, the edge server's constrained processing capabilities present significant difficulties. The IoT-EC scenario is used in this research to study the task scheduling problem, and various jobs are scheduled to virtual machines (VMs) set up the edge server by maximising long-term task satisfaction. The proposed optimal task scheduling considers parameters like makespan, execution time, execution cost, and risk probability. Particularly, the risk probability estimation is done by the deep convolutional neural network (D-CNN). This estimation is based on task security and VM security. The scheduling of tasks is carried out via the new hybrid bald eagle Archimedes optimisation (HBEAO) by considering a multi-objective to minimise the makespan, execution time, execution cost, and risk probability. The proposed model is validated with existing models in terms of execution cost, execution time, fitness, makespan, risk probability, etc. It is observed that the HBEAO model attains less execution cost ($37.27), execution time (0.99 seconds), fitness (3.48%), risk probability (0.19%) and computation time (2,325.87 sec) respectively.

Keywords: task scheduling; deep learning; edge computing; server; optimisation; internet of things; IoT; deep convolutional neural network; D-CNN.

DOI: 10.1504/IJWET.2024.138102

International Journal of Web Engineering and Technology, 2024 Vol.19 No.1, pp.20 - 43

Received: 03 May 2023
Accepted: 06 Dec 2023
Published online: 29 Apr 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article