Power Budgeting of Big Data Applications ...
Document type :
Communication dans un congrès avec actes
Title :
Power Budgeting of Big Data Applications in Container-based Clusters
Author(s) :
Enes, Jonatan [Auteur]
Universidade da Coruña
Fieni, Guillaume [Auteur]
Self-adaptation for distributed services and large software systems [SPIRALS]
Expósito, Roberto [Auteur]
Universidade da Coruña
Rouvoy, Romain [Auteur]
Institut universitaire de France [IUF]
Self-adaptation for distributed services and large software systems [SPIRALS]
Tourino, Juan [Auteur]
Universidade da Coruña
Universidade da Coruña
Fieni, Guillaume [Auteur]
Self-adaptation for distributed services and large software systems [SPIRALS]
Expósito, Roberto [Auteur]
Universidade da Coruña
Rouvoy, Romain [Auteur]

Institut universitaire de France [IUF]
Self-adaptation for distributed services and large software systems [SPIRALS]
Tourino, Juan [Auteur]
Universidade da Coruña
Conference title :
IEEE Cluster 2020
City :
Kobe
Country :
Japon
Start date of the conference :
2020-09-14
Publisher :
IEEE
Publication date :
2020-09-14
English keyword(s) :
Energy consumption
Big Data
Container-based virtualization
Power budget
Resource scaling
Big Data
Container-based virtualization
Power budget
Resource scaling
HAL domain(s) :
Informatique [cs]/Calcul parallèle, distribué et partagé [cs.DC]
English abstract : [en]
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 ...
Show more >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.Show less >
Show more >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.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Collections :
Source :
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