Location
https://www.kennesaw.edu/ccse/events/computing-showcase/fa24-cday-program.php
Event Website
https://github.com/saitsuer/Optimization-of-Fixed-Time-in-Round-Robin-Scheduling-using-Clustering-Algorithms
Document Type
Event
Start Date
19-11-2024 4:00 PM
Description
This project introduces a method to optimize the fixed time in Round Robin scheduling using unsupervised clustering, specifically DBSCAN. Traditionally, fixed time is chosen arbitrarily, often leading to inefficiencies like increased waiting times and frequent context switches. Our approach leverages DBSCAN to identify clusters of processes based on arrival and burst times, as well as to detect outliers that may need unique fixed times. This adaptive, data-driven adjustment has demonstrated improved performance over traditional methods, reducing waiting time, minimizing context switches, and enhancing system throughput. Simulations confirmed the effectiveness of this approach, especially in datasets with outlier processes, where DBSCAN performed exceptionally well.
Included in
GPR-142 Optimization of Fixed Time in Round Robin Scheduling using Clustering Algorithms
https://www.kennesaw.edu/ccse/events/computing-showcase/fa24-cday-program.php
This project introduces a method to optimize the fixed time in Round Robin scheduling using unsupervised clustering, specifically DBSCAN. Traditionally, fixed time is chosen arbitrarily, often leading to inefficiencies like increased waiting times and frequent context switches. Our approach leverages DBSCAN to identify clusters of processes based on arrival and burst times, as well as to detect outliers that may need unique fixed times. This adaptive, data-driven adjustment has demonstrated improved performance over traditional methods, reducing waiting time, minimizing context switches, and enhancing system throughput. Simulations confirmed the effectiveness of this approach, especially in datasets with outlier processes, where DBSCAN performed exceptionally well.
https://digitalcommons.kennesaw.edu/cday/Fall_2024/PhD_Research/3