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SELFWATTS: On-the-fly Selection of Performance Events to Optimize Software-defined Power Meters

Guillaume Fieni 1 Romain Rouvoy 1, 2 Lionel Seinturier 1
1 SPIRALS - Self-adaptation for distributed services and large software systems
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
Abstract : 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.
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Submitted on : Thursday, March 18, 2021 - 2:32:19 PM
Last modification on : Thursday, May 5, 2022 - 10:48:13 AM
Long-term archiving on: : Monday, June 21, 2021 - 8:53:44 AM


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  • HAL Id : hal-03173410, version 1


Guillaume Fieni, Romain Rouvoy, Lionel Seinturier. SELFWATTS: On-the-fly Selection of Performance Events to Optimize Software-defined Power Meters. CCGRID 2021 - 21th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, May 2021, Melbourne, Australia. ⟨hal-03173410⟩



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