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Power Efficiency Containers Scheduling Approach Based on Machine Learning Technique for Cloud Computing Environment

  • Tarek MenouerEmail author
  • Otman Manad
  • Christophe Cérin
  • Patrice Darmon
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1080)

Abstract

Recently, containers have been used extensively in the cloud computing field, and several frameworks have been proposed to schedule containers using a scheduling strategy. The main idea of the different scheduling strategies consist to select the most suitable node, from a set of nodes that forms the cloud platform, to execute each new submitted container. The Spread scheduling strategy, used as the default strategy in the Docker Swarmkit container scheduling framework, consists to select, for each new container, the node with the least number of running containers. In this paper, we propose to improve the Spread strategy by presenting a new container scheduling strategy based on the power consumption of heterogeneous cloud nodes. The novelty of our approach consists to make the best compromise that allows to reduce the global power consumption of an heterogeneous cloud infrastructure. The principle of our strategy is based on learning and scheduling steps which are applied each time a new container is submitted by a user. Our proposed strategy is implemented in Go language inside Docker Swarmkit. Experiments demonstrate the potential of our strategy under different scenarios.

Keywords

Container technology Cloud computing Power Consumption Scheduling strategy 

Notes

Acknowledgment

We thank the Grid5000 team for their help to use the testbed. Grid5000 is supported by a scientific interest group (GIS) hosted by Inria and including CNRS, RENATER and several universities as well as other organizations.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Tarek Menouer
    • 1
    Email author
  • Otman Manad
    • 1
  • Christophe Cérin
    • 2
  • Patrice Darmon
    • 1
  1. 1.UMANISLevallois-PerretFrance
  2. 2.UMR 7030University of Paris 13,LIPN/CNRSVilletaneuseFrance

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