Open Access

ARTICLE

An Enhanced Task Migration Technique Based on Convolutional Neural Network in Machine Learning Framework

Hamayun Khan^1,*, Muhammad Atif Imtiaz², Hira Siddique³, Muhammad Tausif Afzal Rana⁴, Arshad Ali⁵, Muhammad Zeeshan Baig⁶, Saif ur Rehman⁷, Yazed Alsaawy⁵

1 Department of Computer Science, Faculty of Computer Science & IT, Superior University, Lahore, 54000, Pakistan
2 School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW 2522, Australia
3 School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, NSW 2522, Australia
4 School of Information Technology, King’s Own Institute, Sydney, NSW 2000, Australia
5 Faculty of Computer and Information Systems, Islamic University of Madinah, Al Madinah Al Munawarah, 42351, Saudi Arabia
6 Department of Information Technology, Wentworth Institute of Higher Education, Sydney, NSW 2000, Australia
7 Faculty of Electrical Engineering and Technology, Superior University, Lahore, 54000, Pakistan

* Corresponding Author: Hamayun Khan. Email:

Computer Systems Science and Engineering 2025, 49, 317-331. https://doi.org/10.32604/csse.2025.061118

Received 18 November 2024; Accepted 17 January 2025; Issue published 19 March 2025

Abstract

The migration of tasks aided by machine learning (ML) predictions IN (DPM) is a system-level design technique that is used to reduce energy by enhancing the overall performance of the processor. In this paper, we address the issue of system-level higher task dissipation during the execution of parallel workloads with common deadlines by introducing a machine learning-based framework that includes task migration using energy-efficient earliest deadline first scheduling (EA-EDF). ML-based EA-EDF enhances the overall throughput and optimizes the energy to avoid delay and performance degradation in a multiprocessor system. The proposed system model allocates processors to the ready task set in such a way that their deadlines are guaranteed. A full task migration policy is also integrated to ensure proper task mapping that ensures inter-process linkage among the arrived tasks with the same deadlines. The execution of a task can halt on one CPU and reschedule the execution on a different processor to avoid delay and ensure meeting the deadline. Our approach shows promising potential for machine-learning-based schedulability analysis enables a comparison between different ML models and shows a promising reduction in energy as compared with other ML-aware task migration techniques for SoC like Multi-Layer Feed-Forward Neural Networks (MLFNN) based on convolutional neural network (CNN), Random Forest (RF) and Deep learning (DL) algorithm. The Simulations are conducted using super pipelined microarchitecture of advanced micro devices (AMD) XScale PXA270 using instruction and data cache per core 32 Kbyte I-cache and 32 Kbyte D-cache on various utilization factors 12%, 31% and 50%. The proposed approach consumes 5.3% less energy when almost half of the CPU is running and on a lower workload consumes 1.04% less energy. The proposed design accumulatively gives significant improvements by reducing the energy dissipation on three clock rates by 4.41%, on 624 MHz by 5.4% and 5.9% on applications operating on 416 and 312 MHz standard operating frequencies.

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