Comparative study on job scheduling using priority rule and machine learning

Murad, Saydul Akbar and Zafril Rizal, M Azmi and Abu Jafar, Md Muzahid and Al-Imran, Md. (2021) Comparative study on job scheduling using priority rule and machine learning. In: 2021 2021 Emerging Technology in Computing, Communication and Electronics, ETCCE 2021 , 21-23 December 2021 , Dhaka, Bangladesh. pp. 1-8.. ISBN 978-166548364-3

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Abstract

Cloud computing is a potential technique for running resource-intensive applications on a wide scale. Implementation of a suitable scheduling algorithm is critical in order to properly use cloud resources. Shortest Job First (SJF) and Longest Job First (LJF) are two well-known corporate schedulers that are now used to manage Cloud tasks. Although such algorithms are basic and straightforward to develop, they are limited in their ability to deal with the dynamic nature of the Cloud. In our research, we have demonstrated a comparison in our investigations between the priority algorithm performance matrices and the machine learning algorithm. In cloudsim and Google Colab, we finished our experiment. CPU time, turnaround time, wall clock time, waiting time, and execution start time are all included in this research. For time and space sharing mode, the cloudlet is assigned to the CPU. VM is allocated in space-sharing mode all the time. We’ve achieved better for SJF and a decent machine learning algorithm outcome as well.

Item Type:	Conference or Workshop Item (Lecture)
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Cloud computing; Priority rule; Time share; Space share; Adaboost classifier
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Faculty/Division:	Institute of Postgraduate Studies Faculty of Computing
Depositing User:	Mrs Norsaini Abdul Samat
Date Deposited:	04 Jul 2022 02:18
Last Modified:	04 Jul 2022 02:18
URI:	http://umpir.ump.edu.my/id/eprint/34580
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