SELFWATTS: On-the-fly Selection of Performance ...
Document type :
Communication dans un congrès avec actes
Title :
SELFWATTS: On-the-fly Selection of Performance Events to Optimize Software-defined Power Meters
Author(s) :
Fieni, Guillaume [Auteur]
Self-adaptation for distributed services and large software systems [SPIRALS]
Rouvoy, Romain [Auteur]
Institut Universitaire de France [IUF]
Self-adaptation for distributed services and large software systems [SPIRALS]
Seinturier, Lionel [Auteur]
Self-adaptation for distributed services and large software systems [SPIRALS]
Self-adaptation for distributed services and large software systems [SPIRALS]
Rouvoy, Romain [Auteur]

Institut Universitaire de France [IUF]
Self-adaptation for distributed services and large software systems [SPIRALS]
Seinturier, Lionel [Auteur]
Self-adaptation for distributed services and large software systems [SPIRALS]
Conference title :
CCGRID 2021 - 21th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing
City :
Melbourne
Country :
Australie
Start date of the conference :
2021-05-10
English keyword(s) :
Powerapi
containers
Virtual machines
power model
energy
containers
Virtual machines
power model
energy
HAL domain(s) :
Informatique [cs]/Système d'exploitation [cs.OS]
Informatique [cs]/Calcul parallèle, distribué et partagé [cs.DC]
Informatique [cs]/Calcul parallèle, distribué et partagé [cs.DC]
English abstract : [en]
Fine-grained power monitoring of software-defined infrastructures is unavoidable to maximize the power usage efficiency of data centers. However, the design of the underlying power models that estimate the power consumption ...
Show more >Fine-grained power monitoring of software-defined infrastructures is unavoidable to maximize the power usage efficiency of data centers. However, the design of the underlying power models that estimate the power consumption of the monitored software components keeps being a long and fragile process that remains tightly coupled to the host machine and prevents a wider adoption by the industry beyond the rich literature on this topic. To overcome these limitations, this paper introduces SELFWATTS: a lightweight power monitoring system that explores and selects the relevant performance events to automatically optimize the power models to the underlying architecture. Unlike state-of-the-art techniques, SELFWATTS does not require any a priori training phase or specific hardware to configure the power models and can be deployed on a wide range of machines, including heterogeneous environments.Show less >
Show more >Fine-grained power monitoring of software-defined infrastructures is unavoidable to maximize the power usage efficiency of data centers. However, the design of the underlying power models that estimate the power consumption of the monitored software components keeps being a long and fragile process that remains tightly coupled to the host machine and prevents a wider adoption by the industry beyond the rich literature on this topic. To overcome these limitations, this paper introduces SELFWATTS: a lightweight power monitoring system that explores and selects the relevant performance events to automatically optimize the power models to the underlying architecture. Unlike state-of-the-art techniques, SELFWATTS does not require any a priori training phase or specific hardware to configure the power models and can be deployed on a wide range of machines, including heterogeneous environments.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
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