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

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.