DOIONLINE 
DOIONLINE Publish In |
International Journal of Advance Computational Engineering and Networking (IJACEN)-IJACEN |
 Journal Home Volume Issue |
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Issue |
Volume-12,Issue-3 ( Mar, 2024 ) | |||||||||
Paper Title |
MLSCHED: Machine Learning Based Job Scheduler | |||||||||
Author Name |
Sheema Parwaz, Janibul Bashir | |||||||||
Affilition |
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Pages |
61-66 | |||||||||
Abstract |
This paper introduces MLSched, a novel scheduling scheme utilizing machine learning and deep learning techniques, including LSTM, ANN, and Linear Regression. Targeting heterogeneous multicore systems, MLSched enhances throughput by intelligently predicting thread parameters and IPC values for optimal thread scheduling. Compared to existing schemes, MLSched demonstrates a 1.2X speedup and a 20% improvement in system throughput across Parsec and Splash benchmarks, showcasing the effectiveness of machine learning in computer architecture. Keywords - Heterogeneous multiprocessor, Machine learning, Thread Scheduling, Long Short Term Memory | |||||||||
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