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Conference Papers Year : 2020

Power Budgeting of Big Data Applications in Container-based Clusters

Abstract

Energy consumption is currently highly regarded in computing systems for many reasons, such as improving the environmental impact and reducing operational costs considering the rising price of energy. Previous works have analyzed how to improve energy efficiency from the entire infrastructure down to individual computing instances (e.g., virtual machines). However, the research is more scarce when it comes to controlling energy consumption, especially in real-time and at the software level. This paper presents a platform that manages a power budget to cap the energy along several hierarchies, from users to applications and down to individual computing instances. Using software containers as the underlying virtualization technology, the energy limitation is implemented thanks to the platform's ability to monitor container energy consumption and dynamically adjust its CPU resources via vertical scaling as required. Several representative Big Data applications have been deployed on the proposed platform to prove the feasibility of this power budgeting approach for energy control, showing that it is possible to effectively distribute and enforce a power budget among several users and applications.

Dates and versions

hal-02904300 , version 1 (21-07-2020)

Identifiers

Cite

Jonatan Enes, Guillaume Fieni, Roberto R Expósito, Romain Rouvoy, Juan Tourino. Power Budgeting of Big Data Applications in Container-based Clusters. IEEE Cluster 2020, Sep 2020, Kobe, Japan. pp.281-287, ⟨10.1109/CLUSTER49012.2020.00038⟩. ⟨hal-02904300⟩
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