WattsKit: Software-Defined Power Monitoring of Distributed Systems

Abstract : The design and the deployment of energy-efficient distributed systems is a challenging task, which requires software engineers to consider all the layers of a system, from hardware to software. In particular, monitoring and analyzing the power consumption of a distributed system spanning several— potentially heterogeneous—nodes becomes particularly tedious when aiming at a finer granularity than observing the power consumption of hosting nodes. While the state-of-the-art in software-defined power meters fails to deliver adaptive solutions to offer such service-level perspective and to cope with the diversity of hardware CPU architectures, this paper proposes to automatically learn the power models of the nodes supporting a distributed system, and then to use these inferred power models to better understand how the power consumption of the system’s processes is distributed across nodes at runtime. Our solution, named WattsKit, offers a modular toolkit to build software-defined power meters à la carte, thus dealing with the diversity of user and hardware requirements. Beyond the demonstrated capability of covering a wide diversity of CPU architectures with high accuracy, we illustrate the benefits of adopting software-defined power meters to analyze the power consumption of complex layered and distributed systems. In particular, we illustrate the capability of our approach to monitor the power consumption of a system composed of Docker Swarm, Weave, Elastisearch, and Apache Zookeeper. Thanks to WattsKit, developers and administrators can identify potential power leaks in their software infrastructure.
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Contributor : Romain Rouvoy <>
Submitted on : Wednesday, February 8, 2017 - 8:56:20 PM
Last modification on : Thursday, April 4, 2019 - 10:18:05 AM
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Maxime Colmant, Pascal Felber, Romain Rouvoy, Lionel Seinturier. WattsKit: Software-Defined Power Monitoring of Distributed Systems. 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), May 2017, Madrid, Spain. pp.10. ⟨hal-01439889⟩



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