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

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1080)


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.


Container technology Cloud computing Power Consumption Scheduling strategy 



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.


  1. 1.
    Catuogno, L., Galdi, C., Pasquino, N.: Measuring the effectiveness of containerization to prevent power draining attacks. In: 2017 IEEE International Workshop on Measurement and Networking (M&N), pp. 1–6 (2017)Google Scholar
  2. 2.
    Catuogno, L., Galdi, C., Pasquino, N.: An effective methodology for measuring software resource usage. IEEE Trans. Instrum. Meas. 67(10), 2487–2494 (2018)CrossRefGoogle Scholar
  3. 3.
    Choi, S., Myung, R., Choi, H., Chung, K., Gil, J., Yu, H.: Gpsf: general-purpose scheduling framework for container based on cloud environment. In: IEEE International Conference on Internet of Things and IEEE Green Computing and Communications and IEEE Cyber, Physical and Social Computing and IEEE Smart Data, pp. 769–772, Dec (2016)Google Scholar
  4. 4.
    Chung, M.T., Quang-Hung, N., Nguyen, M., Thoai, N.: Using docker in high performance computing applications. In: 2016 IEEE Sixth International Conference on Communications and Electronics (ICCE), pp. 52–57, (July 2016)Google Scholar
  5. 5.
    Clouet, F., et al.: A unified monitoring framework for energy consumption and network traffic. In: TRIDENTCOM - International Conference on Testbeds and Research Infrastructures for the Development of Networks & Communities, p. 10. Vancouver, Canada (June 2015)Google Scholar
  6. 6.
    Grid5000: Accesssed 25 Mar 2019
  7. 7.
    Hindman, B., et al.: Mesos: a platform for fine-grained resource sharing in the data center. In: NSDI, pp. 22–22 (2011)Google Scholar
  8. 8.
    Kaewkasi, C., Chuenmuneewong, K.: Improvement of container scheduling for docker using ant colony optimization. In: 2017 9th International Conference on Knowledge and Smart Technology (KST), pp. 254–259 (Feb 2017)Google Scholar
  9. 9.
    Lin, W., Xu, S., Li, J., Xu, L., Peng, Z.: Design and theoretical analysis of virtual machine placement algorithm based on peak workload characteristics. Soft Comput. 21(5), 1301–1314 (2017)CrossRefGoogle Scholar
  10. 10.
    Lin, W., Zhu, C., Li, J., Liu, B., Lian, H.: Novel algorithms and equivalence optimisation for resource allocation in cloud computing. Int. J. Web Grid Serv. 11, 193 (2015)CrossRefGoogle Scholar
  11. 11.
    Liu, B., Li, P., Lin, W., Shu, N., Li, Y., Chang, V.: A new container scheduling algorithm based on multi-objective optimization. Soft Comput. 22, 1–12 (2018)CrossRefGoogle Scholar
  12. 12.
    Maaouia, O., Fkaier, H., Cérin, C., Jemni, M., Ngoko, Y.: On optimization of energy consumption in a volunteer cloud. In: 18th International Conference, ICA3PP 2018, Guangzhou, China, November 15–17, 2018, Proceedings, Part II, pp. 388–398. (Nov 2018)Google Scholar
  13. 13.
    Menouer, T., Darmon, P.: A new container scheduling algorithm based on multi-objective optimization. In: 27th Euromicro International Conference on Parallel, Distributed and Network-based Processing, Pavia, Italy, (Feb 2019)Google Scholar
  14. 14.
    Merkel, D.: Docker: lightweight linux containers for consistent development and deployment. Linux J. 2014(239), 2 (2014)Google Scholar
  15. 15.
    Pei, J.H.M.K.J.: Data Mining: Concepts and Techniques, 3rd edn. Elsevier, Amsterdam (2011)Google Scholar
  16. 16.
    Sotiriadis, S., Bessis, N., Buyya, R.: Self managed virtual machine scheduling in cloud systems. Inf. Sci. 433–434, 381–400 (2018)CrossRefGoogle Scholar
  17. 17.
    Sureshkumar, M., Rajesh, P.: Optimizing the docker container usage based on load scheduling. In: 2017 2nd International Conference on Computing and Communications Technologies (ICCCT), pp. 165–168. (Feb 2017)Google Scholar
  18. 18.
    Ullman, J.: Np-complete scheduling problems. J. Comput. Syst. Sci. 10(3), 384–393 (1975)MathSciNetCrossRefGoogle Scholar
  19. 19.
    The apache software foundation. mesos, apache: Accessed 25 Mar 2019
  20. 20.
    Docker swarmkit: Accessed 25 Mar 2019
  21. 21.
    Kubernetes scheduler: Accessed 25 Mar 2019

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.UMANISLevallois-PerretFrance
  2. 2.UMR 7030University of Paris 13,LIPN/CNRSVilletaneuseFrance

Personalised recommendations