dc.contributor.advisor | Chakrabarty, Amitabha | |
dc.contributor.advisor | Mostakim, Moin | |
dc.contributor.author | Niloy, Maharshi | |
dc.contributor.author | Moni, Md. Moynul Asik | |
dc.contributor.author | Khan, Farah Jasmin | |
dc.contributor.author | Chowdhury, Aquibul Haq | |
dc.contributor.author | Juboraj, Md. Fahmid-Ul-Alam | |
dc.date.accessioned | 2023-10-16T05:33:25Z | |
dc.date.available | 2023-10-16T05:33:25Z | |
dc.date.copyright | ©2022 | |
dc.date.issued | 2022-09-28 | |
dc.identifier.other | ID 19101117 | |
dc.identifier.other | ID 19101189 | |
dc.identifier.other | ID 19101239 | |
dc.identifier.other | ID 19101290 | |
dc.identifier.other | ID 19101618 | |
dc.identifier.uri | http://hdl.handle.net/10361/21838 | |
dc.description | This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. | en_US |
dc.description | Cataloged from PDF version of thesis. | |
dc.description | Includes bibliographical references (pages 36-39). | |
dc.description.abstract | 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. | en_US |
dc.description.statementofresponsibility | Maharshi Niloy | |
dc.description.statementofresponsibility | Md. Moynul Asik Moni | |
dc.description.statementofresponsibility | Farah Jasmin Khan | |
dc.description.statementofresponsibility | Aquibul Haq Chowdhury | |
dc.description.statementofresponsibility | Md. Fahmid-Ul-Alam Juboraj | |
dc.format.extent | 51 pages | |
dc.language.iso | en | en_US |
dc.publisher | Brac University | en_US |
dc.rights | 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. | |
dc.subject | Decision tree (DT) | en_US |
dc.subject | KNN | en_US |
dc.subject | Linear regression (LR) | en_US |
dc.subject | Neural network (NN) | en_US |
dc.subject | MLP | en_US |
dc.subject | Average turnaround time (avg TT) | en_US |
dc.subject | Optimized round robin (ORR) | en_US |
dc.subject | Modified round robin algorithm (MRRA) | en_US |
dc.subject | Self-adjustment round robin (SARR) | en_US |
dc.subject | Round robin (RR) | en_US |
dc.subject | Average waiting time (avg WT) | en_US |
dc.subject | Context switch (CS) | en_US |
dc.subject | Proposed algorithm | en_US |
dc.subject | Burst time | en_US |
dc.subject.lcsh | Artificial intelligence | |
dc.subject.lcsh | Algorithms | |
dc.title | Comparative analysis and implementation of AI algorithms and NN model in process scheduling algorithm | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Department of Computer Science and Engineering, Brac University | |
dc.description.degree | B.Sc. in Computer Science | |