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.

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}
}