Paper Title :MLSCHED: Machine Learning Based Job Scheduler

Author :Sheema Parwaz, Janibul Bashir

Article Citation :Sheema Parwaz ,Janibul Bashir , (2024 ) " MLSCHED: Machine Learning Based Job Scheduler " , International Journal of Advance Computational Engineering and Networking (IJACEN) , pp. 61-66, Volume-12,Issue-3

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

Type : Research paper

Published : Volume-12,Issue-3

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