A control-theory approach for cluster autonomic management: maximizing usage while avoiding overload

Yabo, Agustín Gabriel; Robu, Bogdan; Richard, Olivier; Bzeznik, Bruno; Rutten, Éric

doi:10.1109/ccta.2019.8920473

Cited by 4 publications

(5 citation statements)

References 18 publications

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“…In Computer Science and Software Engineering, Autonomic Computing [2] is addressing this concern, proposing software architectures with feedback loops involving a variety of decision mechanisms (e.g., programmatic, based on AI and Machine Learning, scheduling, constraint programming). A particularly interesting approach involves Control Theory [3]- [5] with applications to HPC e.g., [6]- [8].…”

Section: A Need Of Control Theory For High Performance Computingmentioning

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: A Need Of Control Theory For High Performance Computingmentioning

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)

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“…Its use for HPC systems is recent, however. For example, Yabo et al [9] used a control theory approach for management of scientific workflows in HPC systems. Recent works on energy management of computing systems use control theory.…”

Section: A Control Theory For High-performance Computingmentioning

confidence: 99%

Adaptive Power Control for Sober High-Performance Computing

Hawila

Cerf

Bleuse

et al. 2022

2022 IEEE Conference on Control Technology and Applications (CCTA)

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Soberness-in terms of electrical power-of data centers and high-performance computing systems is becoming an important design issue, as the global energy consumption of information technologies is rising at considerable levels. This issue is all the more complex as these systems are increasingly heterogeneous and variable in their behavior, for example, w.r.t. performance and power consumption, and less predictable, thus demanding runtime management and feedback control.This paper addresses the problem of the control of the power allocated to processors and hence their energy consumption and performance. The use of feedback control allows the energy consumption to be reduced by decreasing the speed without losing performance, by exploiting periods where read/write operations slow the progress. Previous works present limitations regarding both modeling (nonlinear models with numerous parameters) and control performance (mainly instability caused by platform variations). We develop a novel adaptive control that is robust to the variety of execution platforms while maintaining the existing global goals of energy management. We evaluate-on a real system using the Grid'5000 testbed-the robustness of the control to changes in initial parameters and to disturbances, and we compare it with the previous proportionalintegral (PI) control. Our adaptive control approach allows for up to 25% energy savings.

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“…The work done in [13] used a simplistic approximation of the behavior of the system coupled with an MPC controller. However, experiments showed that the model is far from reality and, does not scale up and behaves incorrectly when the size of the cluster is above 15-20 nodes.…”

Section: Proposed Solutionmentioning

confidence: 99%

“…The goal of the following experiments is to compare the solution proposed in Section III with the solutions implemented in previous works [13] and presented in III-A. We will measure the number of jobs sent by CiGri at each submission, the load of the file-server, as well as the number of running jobs in the cluster.…”

Section: A Experimental Goalsmentioning

confidence: 99%

“…In [12], a Proportional-Integral controller was developed in order to control the number of jobs sent to the cluster from CiGri but without taking into account the load of the fileserver which is the most important and by considering a very small number of computing resources which is not realistic for an HPC environment. A MPC approach based on a very simplistic approximation of the behavior of our system: cluster and file-server, was used in [13] but it showed again its limitation while scaling up with the size of the cluster and the number of the high priority users. The remaining of this paper extends the previous work by introducing new approach for the regulation of the file-server load by taking into account the I/O weight of the Bag-of-Tasks applications while harvesting idle resources.…”

Section: Introductionmentioning

confidence: 99%

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