Controlling the Injection of Best-Effort Tasks to Harvest Idle Computing Grid Resources

Guilloteau, Quentin; Richard, Olivier; Robu, Bogdan; Rutten, Éric

doi:10.1109/icstcc52150.2021.9607292

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…We can also note that for campaigns with larger I/O, the response of the controller is faster but also more aggressive. We want in this Section to evaluate the advantage of using our solution compared to the original CiGri submission mechanism [7] presented in Section II-B.1. We will run the MADbench2 application [23] as a premium user's application to assess of the impact of the idle resources harvesting.…”

Section: Controller Robustness To External Disturbancesmentioning

confidence: 99%

Model-Free Control for Resource Harvesting in Computing Grids

Guilloteau

Robu

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et al. 2022

2022 IEEE Conference on Control Technology and Applications (CCTA)

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Cloud and High-Performance Computing (HPC) systems are increasingly facing issues of dynamic variability, in particular w.r.t. performance and power consumption. They are becoming less predictable, and therefore demand more runtime management by feedback loops. In this work, we describe results addressing autonomic administration in HPC systems through a control theoretical approach. We more specifically consider the need for controllers that can adapt to variations a long timee in the behavior of controlled systems, but also to being reused on different systems and processors. We therefore explore the application of Model-Free Control (MFC) in the context of resource harvesting in a Computing Grid, by regulating the injection of flexible jobs while limiting perturbation of the prioritary applications.

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Section: Controller Robustness To External Disturbancesmentioning

confidence: 99%

Model-Free Control for Resource Harvesting in Computing Grids

Guilloteau

Robu

Join³

et al. 2022

2022 IEEE Conference on Control Technology and Applications (CCTA)

Self Cite

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Making Control in High Performance Computing for Overload Avoidance Adaptive in Time and Job Size

Pagano,

Cerf,

Robu

et al. 2024

2024 IEEE Conference on Control Technology and Applications (CCTA)

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The feedback control of High-Performance Computing (HPC) has been explored as an application area of Control Theory, because of the high variability involved in their resource management. A regulation mechanism can allow to soundly automate the injection of small flexible jobs in a cluster. A trade-off is needed, to fill up the cluster's computing capacity while avoiding overload of e.g., the file server.In this work, we describe new results in this context, where the overload avoidance controller is made adaptive to the jobs' size, that is a time-varying unknown parameter. To do so, the original PI controller is enhanced with an online estimation algorithm that allows the controller to adapt to various working conditions, to avoid performance degradation. Parallel and robust estimation algorithms are designed, tackling the challenges of bursting and noise in the system. Validation and evaluation of the adaptive controller are performed on a large-scale experimental HPC platform, showing higher robustness than the state-of-the-art in highly varying conditions. Reproducible analysis are available at doi:10.5281/zenodo.11961696.

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