HBalancer: A machine learning based load balancer in real time CPU-GPU heterogeneous systems

Rahmani, Taha Abdelaziz; Daham, Fatima; Belalem, Ghalem; Mahmoudi, Sidi

doi:10.1109/3ict56508.2022.9990623

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Article (Scientific journals)

HBalancer: A machine learning based load balancer in real time CPU-GPU heterogeneous systems

Rahmani, Taha Abdelaziz; Daham, Fatima; Belalem, Ghalem et al.

2022 • In 2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)

Peer reviewed

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https://hdl.handle.net/20.500.12907/44863

DOI
10.1109/3ict56508.2022.9990623

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Keywords :

Intelligence Artificielle; GPU; CPU; machine learning, heteogeneous systems

Disciplines :

Computer science

Author, co-author :

Rahmani, Taha Abdelaziz; Ahmed Ben Bella Oran 1 University,LIO Laboratory,Dept. Computer Science,Oran,Algeria

Daham, Fatima; Ahmed Ben Bella Oran 1 University,Dept. Computer Science,Oran,Algeria

Belalem, Ghalem; Ahmed Ben Bella Oran 1 University,LIO Laboratory,Dept. Computer Science,Oran,Algeria

Mahmoudi, Sidi ; Université de Mons - UMONS

Language :

English

Title :

HBalancer: A machine learning based load balancer in real time CPU-GPU heterogeneous systems

Publication date :

20 November 2022

Journal title :

2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)

ISSN :

2770-7466

Publisher :

IEEE

Peer reviewed :

Peer reviewed

Additional URL :

http://xplorestaging.ieee.org/ielx7/9989532/9990057/09990623.pdf?arnumber=9990623

Research institute :

R300 - Institut de Recherche en Technologies de l'Information et Sciences de l'Informatique
R450 - Institut NUMEDIART pour les Technologies des Arts Numériques

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since 14 January 2023

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Bibliography

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Yasir N. K., Muhammad A., Usman A., Muhammad A. I., Muhammad A. I., Troodon: A machine-learning based load-balancing application scheduler for CPU-GPU system, Journal of Parallel and Distributed Computing, Volume 132, Pages 79-94, https://doi. org/10. 1016/j. jpdc. 2019. 05. 015.
Mahmoudi Sidi., Manneback P., Augonnet C., Thibault S., (2012),. Traitements d'Images sur Architectures Parallèles et Hétérogènes. Techniques et sciences informatiques Volume 31 pages 1183-1203. https://doi. org/10. 3166/tsi. 31. 1183-1203 (in French).
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Harichane I., Makhlouf SA., Belalem G., (2022). KubeSC-RTP: Smart scheduler for Kubernetes platform on CPU-GPU heterogeneous systems. Concurrency and Computation Practice and Experience e7108, https://doi. org/10. 1002/cpe. 7108.
Grauer-Gray S., Xu L., Searles R., Ayalasomayajula S., Cavazos J., (2012), "Auto-tuning a high-level language targeted to GPU codes, Innovative Parallel Computing (InPar), pages 1-10. https://doi. org/10. 1109/InPar. 2012. 6339595.
Moez A., (2020). PyCaret: An open source, low-code machine learning library in Python. https://www. pycaret. org/.
Borja P., Stafford E., Bosque J. L., Beivide R., (2021), Sigmoid: An auto-tuned load balancing algorithm for heterogeneous systems, Journal of Parallel and Distributed Computing, Volume 157, 2021, Pages 30-42, https://doi. org/10. 1016/j. jpdc. 2021. 06. 003.
Khalid, Y. N., Aleem, M., Prodan, (2018), E-OSched: A load balancing scheduler for heterogeneous multicores. Journal of Supercomputing Volume 74, pages 5399-5431. https://doi. org/10. 1007/s11227-018-2435-1.