Predicting the efficiency of job scheduler actions
Resumo
The order in which tasks are executed in High Performance Computing (HPC) infrastructures is fundamental to the efficiency of the virtual environment. This article covera a amachine learning- and polynomial regression-aided effort of predicting and thus, drawing out a better understanding of, the individual performances of various job scheduling algorithms.
Referências
Brucker, P. (1999). Scheduling algorithms. Journal-Operational Research Society, 50:774–774.
Carastan-Santos, D. and de Camargo, R. Y. (2017). Obtaining Dynamic Scheduling Policies with Simulation and Machine Learning. In SC’17 -2 International Conference for High Performance Computing, Networking, Storage and Analysis (Supercomputing), Denver, United States.
Casagrande, L., Koslovski, G., Miers, C.C., P., M.A., and Gonzalez, N. (2022). Don’t hurry be green: scheduling servers shutdown in grid computing with deep reinforcement learning. In International Journal of Grid and Utility Computing. Inderscience Publishers.
Casanova, H. (2001). Simgrid: A toolkit for the simulation of application scheduling. In Proceedings First IEEE/ACM International Symposium on Cluster Computing and the Grid, pages 430–437. IEEE.
Chapin, S. J., Cirne, W., Feitelson, D. G., Jones, J. P., Leutenegger, S. T., Schwiegelshohn, U., Smith, W., and Talby, D. (1999). Benchmarks and standards for the evaluation of parallel job schedulers. In Feitelson, D. G. and Rudolph, L., editors, Job Scheduling Strategies for Parallel Processing, pages 67–90, Berlin, Heidelberg. Springer Berlin Heidelberg.
Shyalika, C., Silva, T., and Karunananda, A. (2020). Reinforcement learning in dynamic task scheduling: A review. SN Computer Science, 1:306.
Carastan-Santos, D. and de Camargo, R. Y. (2017). Obtaining Dynamic Scheduling Policies with Simulation and Machine Learning. In SC’17 -2 International Conference for High Performance Computing, Networking, Storage and Analysis (Supercomputing), Denver, United States.
Casagrande, L., Koslovski, G., Miers, C.C., P., M.A., and Gonzalez, N. (2022). Don’t hurry be green: scheduling servers shutdown in grid computing with deep reinforcement learning. In International Journal of Grid and Utility Computing. Inderscience Publishers.
Casanova, H. (2001). Simgrid: A toolkit for the simulation of application scheduling. In Proceedings First IEEE/ACM International Symposium on Cluster Computing and the Grid, pages 430–437. IEEE.
Chapin, S. J., Cirne, W., Feitelson, D. G., Jones, J. P., Leutenegger, S. T., Schwiegelshohn, U., Smith, W., and Talby, D. (1999). Benchmarks and standards for the evaluation of parallel job schedulers. In Feitelson, D. G. and Rudolph, L., editors, Job Scheduling Strategies for Parallel Processing, pages 67–90, Berlin, Heidelberg. Springer Berlin Heidelberg.
Shyalika, C., Silva, T., and Karunananda, A. (2020). Reinforcement learning in dynamic task scheduling: A review. SN Computer Science, 1:306.
Publicado
24/04/2024
Como Citar
KRAUS, Ana Eloina Nascimento; DIEL, Guilerme; KOSLOVSKI, Guilherme Piêgas.
Predicting the efficiency of job scheduler actions. In: ESCOLA REGIONAL DE ALTO DESEMPENHO DA REGIÃO SUL (ERAD-RS), 24. , 2024, Florianópolis/SC.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 33-36.
ISSN 2595-4164.
DOI: https://doi.org/10.5753/eradrs.2024.238711.
