Cloud Brain Fog Scheduler Machine Learning-Enhanced Task Allocation

International Journal of Electronics and Communication Engineering

© 2024 by SSRG - IJECE Journal

Volume 11 Issue 7

Year of Publication : 2024

Authors : Hardik Mahendrabhai Patel, Kirit J. Modi, Chirag I. Patel

10.14445/23488549/IJECE-V11I7P120

How to Cite?

Hardik Mahendrabhai Patel, Kirit J. Modi, Chirag I. Patel, "Cloud Brain Fog Scheduler Machine Learning-Enhanced Task Allocation," SSRG International Journal of Electronics and Communication Engineering, vol. 11,  no. 7, pp. 200-207, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I7P120

Abstract:

Fog computing bridges the advantages of cloud computing with edge computing, enhancing service delivery, reducing latency, and supporting mobility. Despite facing challenges in security, service management, operation, and data handling, fog computing holds promise in sectors like agriculture, urban planning, and healthcare. This paper introduces an innovative machine learning algorithm designed to aid cloud platforms in selecting optimal scheduling strategies through multi-criteria decision-making, thereby improving performance optimization. Our primary objective is to minimize the makespan for a given set of tasks. To assess the effectiveness of our approach, we perform simulations using the CloudSim toolkit, examining the algorithm’s performance across various configurations, including different numbers of Virtual Machines (VMs).

Keywords:

Machine learning, Cloud computing, Fog computing, Real time task scheduling, Energy efficient, Distributed system.

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