Towards a control-theory approach for minimizing unused grid resources - SYSCO Accéder directement au contenu
Communication Dans Un Congrès Année : 2018

Towards a control-theory approach for minimizing unused grid resources

Résumé

HPC systems are facing more and more variability in their behavior, related to e.g., performance and power consumption, and the fact that they are less predictable requires more runtime management. This can be done in an Autonomic Management feedback loop, in response to monitored information in the systems, by analysis of this data and utilization of the results in order to activate appropriate system-level or application-level feedback mechanisms (e.g., informing schedulers, down-clocking CPUs). One such problem is found in the context of CiGri, a simple, lightweight, scalable and fault tolerant grid system which exploits the unused resources of a set of computing clusters. Computing power left over by the execution of a main HPC application scheduling is used to execute smaller jobs, which are injected as much as the global system allows. This paper presents rst results addressing the problem of au- tomated resource management in an HPC infrastructure, using techniques from Control Theory to design a controller that maximizes cluster utilization while avoiding overload. We put in place a mechanism for feedback (Proportional Integral, PI) control system software, through a maximum number of jobs to be sent to the cluster, in response to system information about the current number of jobs processed.
Fichier principal
Vignette du fichier
CtrlCiGri.pdf (375.76 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01823787 , version 1 (18-12-2018)

Identifiants

Citer

Emmanuel Stahl, Agustín Gabriel Yabo, Olivier Richard, Bruno Bzeznik, Bogdan Robu, et al.. Towards a control-theory approach for minimizing unused grid resources. AI-Science'18 - workshop on Autonomous Infrastructure for Science, in conjunction with the ACM HPDC 2018, Jun 2018, Tempe, AZ, United States. pp.1-8, ⟨10.1145/3217197.3217201⟩. ⟨hal-01823787⟩
348 Consultations
256 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More