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DOI: 10.14569/IJACSA.2019.0100377
PDF

Spin-Then-Sleep: A Machine Learning Alternative to Queue-based Spin-then-Block Strategy

Author 1: Fadai Ganjaliyev

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 3, 2019.

  • Abstract and Keywords
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Abstract: One of the issues with spinlock protocols is excessive spinning which results in a waste of CPU cycles. Some protocols use the hybrid, spin-then-block approach to avoid this problem. In this case, the contending thread may prefer relinquishing the CPU instead of spinning, and resumes execution once notified. This paper presents a machine learning framework for intelligent sleeping and spinning as an alternative to the spin-then-block strategy. This framework can be used to address one of the challenges faced by this strategy: the delay in the critical path. The work suggests a reinforcement learning based approach for queue-based locks that aims at having threads learn to spin or sleep. The challenges of the suggested technique and future work are also discussed.

Keywords: Spinlock; spin-then-block; reinforcement learning; queue-based lock; intelligent sleeping

Fadai Ganjaliyev, “Spin-Then-Sleep: A Machine Learning Alternative to Queue-based Spin-then-Block Strategy” International Journal of Advanced Computer Science and Applications(IJACSA), 10(3), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100377

@article{Ganjaliyev2019,
title = {Spin-Then-Sleep: A Machine Learning Alternative to Queue-based Spin-then-Block Strategy},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100377},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100377},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {3},
author = {Fadai Ganjaliyev}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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