Comparative Analysis of Process Scheduling Algorithm using AI models

Process scheduling is an integral part of operating systems. The most widely used scheduling algorithm in operating systems is Round Robin, but the average waiting time in RR is often quite long. The purpose of this study is to propose a new algorithm to minimize waiting time and process starvation...

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Vydáno v:2022 25th International Conference on Computer and Information Technology (ICCIT) s. 587 - 592
Hlavní autoři: Moni, Md. Moynul Asik, Niloy, Maharshi, Chowdhury, Aquibul Haq, Khan, Farah Jasmin, Juboraj, Md. Fahmid-Ul-Alam, Chakrabarty, Amitabha
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 17.12.2022
Témata:
Absolute Difference Based Time Quantum Round Robin
Average Turnaround Time
Average Waiting Time
Context Switch
Machine learning algorithms
Operating systems
Optimized Round Robin
Prediction algorithms
Predictive models
Quantum computing
Scheduling
Self-Adjustment Round Robin
Switches
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Shrnutí:Process scheduling is an integral part of operating systems. The most widely used scheduling algorithm in operating systems is Round Robin, but the average waiting time in RR is often quite long. The purpose of this study is to propose a new algorithm to minimize waiting time and process starvation by determining the optimal time quantum by predicting CPU burst time. For burst time prediction, we are using the machine learning algorithms like linear regression, decision tree, k-nearest neighbors, and Neural Network Model Multi-Layer Perceptron. Moreover, for 10000 predicted burst time of processes with the same configuration, we have compared the average turnaround time, the average waiting time and the number of context switches of the proposed modified round robin algorithm with Traditional Round Robin, Modified Round Robin, Optimized Round Robin and Self-Adjustment Round Robin. The proposed modified round robin i.e. Absolute Difference Based Time Quantum Round Robin (ADRR) is found to be almost 2 times faster than the other algorithm in terms of process scheduling for the used dataset which contains a huge load of processes.
DOI:10.1109/ICCIT57492.2022.10055395