Synchronous Q-Learning Algorithm for Enhanced Load Balancing in Multi-Core Processors

Volume 19 No 1 (2021)

Avinash Dhole and Shailendra Verma

DOI: 10.48047/nq.2021.19.1.NQ21050

Abstract

Conventional processors are extensively used for applications like weather forecasting, AI, ocean modeling, and big data analysis. This research investigates parallel computing strategies aimed at reducing execution time. We developed a new dynamic load balancing technique by integrating Classical Q-Learning with Modified Q-Learning, termed Synchronous Q-Learning. Simulation results demonstrate that this method reduces execution time in multi-core systems. The Synchronous Q-Learning algorithm combines policy iteration and classical Q-Learning to optimize load balancing through reinforcement learning.

Keywords

Multi-core Processing, Reinforcement Learning, Load Balancing, Machine Learning.

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