Comparative analysis and implementation of AI algorithms and NN model in process scheduling algorithm
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.
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Brac University
2023
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| Rochtain ar líne: | http://hdl.handle.net/10361/21838 |
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10361-218382023-10-16T21:04:27Z Comparative analysis and implementation of AI algorithms and NN model in process scheduling algorithm Niloy, Maharshi Moni, Md. Moynul Asik Khan, Farah Jasmin Chowdhury, Aquibul Haq Juboraj, Md. Fahmid-Ul-Alam Chakrabarty, Amitabha Mostakim, Moin Department of Computer Science and Engineering, Brac University Decision tree (DT) KNN Linear regression (LR) Neural network (NN) MLP Average turnaround time (avg TT) Optimized round robin (ORR) Modified round robin algorithm (MRRA) Self-adjustment round robin (SARR) Round robin (RR) Average waiting time (avg WT) Context switch (CS) Proposed algorithm Burst time Artificial intelligence Algorithms This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 36-39). Process scheduling is an integral part of operating systems. The most widely used scheduling algorithm in operating systems is round-robin (RR), 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 decision tree (DT), k-nearest neighbors (KNN), linear regression (LR) and Neural Network Model Multi-Layer perceptron-MLP. Finally, the obtained accuracy for burst time prediction of DT is 98.64%, KNN is 17.1%, LR is 97.96% and using MLP is 26.01%. Moreover, for 10000 predicted(burst time) processes with the same configuration the average turnaround time (avg TT), the average wait time (avg WT) and the number of context switches (CS) of the proposed algorithm are consecutively 40331930.48, 40312117.96 and 20002, whereas Traditional Round Robin (RR) has 87194390.98 (avg TT), 87174578.46 (avg WT) and 28964 (CS). Self-Adjustment Round Robin (SARR) has 72398064.70 (avg TT), 72378252.18 (avg WT) and 39956 (CS). Modi- fied Round Robin Algorithm (MRRA) has 84924105.36 (avg TT), 84904292.84 (avg WT) and 5208 (CS) and Optimized Round Robin (ORR) has 78508779.73 (avg TT), 78488967.20 (avg WT) and 22470 (CS). Therefore, it is clear that the proposed algo- rithm is almost 2 times faster than the other algorithm in terms of process scheduling under a huge load of processes. Maharshi Niloy Md. Moynul Asik Moni Farah Jasmin Khan Aquibul Haq Chowdhury Md. Fahmid-Ul-Alam Juboraj B.Sc. in Computer Science 2023-10-16T05:33:25Z 2023-10-16T05:33:25Z ©2022 2022-09-28 Thesis ID 19101117 ID 19101189 ID 19101239 ID 19101290 ID 19101618 http://hdl.handle.net/10361/21838 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 51 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Decision tree (DT) KNN Linear regression (LR) Neural network (NN) MLP Average turnaround time (avg TT) Optimized round robin (ORR) Modified round robin algorithm (MRRA) Self-adjustment round robin (SARR) Round robin (RR) Average waiting time (avg WT) Context switch (CS) Proposed algorithm Burst time Artificial intelligence Algorithms |
| spellingShingle |
Decision tree (DT) KNN Linear regression (LR) Neural network (NN) MLP Average turnaround time (avg TT) Optimized round robin (ORR) Modified round robin algorithm (MRRA) Self-adjustment round robin (SARR) Round robin (RR) Average waiting time (avg WT) Context switch (CS) Proposed algorithm Burst time Artificial intelligence Algorithms Niloy, Maharshi Moni, Md. Moynul Asik Khan, Farah Jasmin Chowdhury, Aquibul Haq Juboraj, Md. Fahmid-Ul-Alam Comparative analysis and implementation of AI algorithms and NN model in process scheduling algorithm |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. |
| author2 |
Chakrabarty, Amitabha |
| author_facet |
Chakrabarty, Amitabha Niloy, Maharshi Moni, Md. Moynul Asik Khan, Farah Jasmin Chowdhury, Aquibul Haq Juboraj, Md. Fahmid-Ul-Alam |
| format |
Thesis |
| author |
Niloy, Maharshi Moni, Md. Moynul Asik Khan, Farah Jasmin Chowdhury, Aquibul Haq Juboraj, Md. Fahmid-Ul-Alam |
| author_sort |
Niloy, Maharshi |
| title |
Comparative analysis and implementation of AI algorithms and NN model in process scheduling algorithm |
| title_short |
Comparative analysis and implementation of AI algorithms and NN model in process scheduling algorithm |
| title_full |
Comparative analysis and implementation of AI algorithms and NN model in process scheduling algorithm |
| title_fullStr |
Comparative analysis and implementation of AI algorithms and NN model in process scheduling algorithm |
| title_full_unstemmed |
Comparative analysis and implementation of AI algorithms and NN model in process scheduling algorithm |
| title_sort |
comparative analysis and implementation of ai algorithms and nn model in process scheduling algorithm |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/21838 |
| work_keys_str_mv |
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| _version_ |
1814308339049299968 |