Skip to main content
SpringerLink
Log in

High-performance docker integration scheme based on OpenStack

  • Published:
World Wide Web Aims and scope Submit manuscript

Abstract

As an emerging technology in cloud computing Docker is becoming increasingly popular due to its high speed high efficiency and portability. The integration of Docker with OpenStack has been a hot topic in research and industrial areas e.g. as an emulation platform for evaluating cyberspace security technologies. This paper introduces a high-performance Docker integration scheme based on OpenStack that implements a container management service called Yun. Yun interacts with OpenStack’s services and manages the lifecycle of the container through the Docker Engine to integrate OpenStack and Docker. Yun improves the container deployment and throughput as well as the system performance by optimizing the message transmission architecture between internal components the underlying network data transmission architecture between containers and the scheduling methods. Based on the Docker Engine API Yun provides users with interfaces for CPU memory and disk resource limits to satisfy precise resource limits. Regarding scheduling Yun introduces a new NUMA-aware and resource-utilization-aware scheduling model to improve the performance of containers under resource competition and to balance the load of computing resources. Simultaneously Yun decouples from OpenStack versions by isolating its own running environment from the running environment of OpenStack to achieve better compatibility. Experiments show that compared to traditional methods Yun not only achieves the integration of OpenStack and Docker but also exhibits high performance in terms of deployment efficiency container throughput and the container’s system while also achieving load balancing.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20

Similar content being viewed by others

References

  1. Al-Roomi M., Al-Ebrahim S., Buqrais S., Ahmad I.: Cloud computing pricing models: a survey. Int. J. Grid Distrib. Comput. 6(5), 93–106 (2013)

    Article  Google Scholar 

  2. Anderson C.: Docker [software engineering]. IEEE Softw. 32(3), 102–c3 (2015)

    Article  Google Scholar 

  3. Virtualenv B.I.: The Open Planning Project pyPA (2014)

  4. Biederman E.W., Networx L.: Multiple instances of the global linux namespaces. In: Proceedings of the Linux Symposium vol. 1 pp. 101–112. Citeseer (2006)

  5. Calheiros R.N., Masoumi E., Ranjan R., Buyya R.: Workload prediction using arima model and its impact on cloud applications’ qos. IEEE Trans. Cloud Comput. 3 (4), 449–458 (2015)

    Article  Google Scholar 

  6. Calinciuc A., Spoiala C.C., Turcu C.O., Filote C.: Openstack and Docker: Building a High-Performance Iaas Platform for Interactive Social Media Applications. In: 2016 International Conference on Development and Application Systems (DAS) pp. 287–290. IEEE (2016)

  7. Callegati F., Cerroni W., Contoli C., Santandrea G.: Performance of Network Virtualization in Cloud Computing Infrastructures: The Openstack Case. In: 2014 IEEE 3rd International Conference on Cloud Networking (Cloudnet) pp. 132–137. IEEE (2014)

  8. Cen S., Bo L., Feng W., Hui D., Wei D., Wei S., Zhang X., Zhi Y.: Openstack Platform and Its Application in Big Data Processing. In: International Conference on Intelligent Networks & Intelligent Systems (2016)

  9. Cheng Y., Chen W., Wang Z., Yu X.: Performance-monitoring-based traffic-aware virtual machine deployment on numa systems. IEEE Syst. J. 11(2), 973–982 (2017)

    Article  Google Scholar 

  10. Coullon H., Perez C., Pertin D.: Production Deployment Tools for Iaases: an Overall Model and Survey. In: 2017 IEEE 5th International Conference on Future Internet of Things and Cloud (Ficloud) pp. 183–190. IEEE (2017)

  11. Docker: storagedriver. https://docs.docker.com/storage/storagedriver/

  12. Dutta A., Gnawali O.: Large-Scale Network Protocol Emulation on Commodity Cloud. In: 2014 IEEE Global Communications Conference pp. 1114–1119. IEEE (2014)

  13. Hirt T.: Kvm-the kernel-based virtual machine. Red Hat Inc (2010)

  14. Huang W., Zhang W., Zhang D., Meng L.: Elastic spatial query processing in openstack cloud computing environment for time-constraint data analysis. ISPRS Int. J. Geo-Inf. 6(3), 84 (2017)

    Article  Google Scholar 

  15. iperf: esnet. https://github.com/esnet/iperf

  16. Jansen C., Witt M., Krefting D.: Employing Docker Swarm on Openstack for Biomedical Analysis. In: International Conference on Computational Science and Its Applications pp. 303–318. Springer (2016)

  17. Kozhirbayev Z., Sinnott R.O.: A performance comparison of container-based technologies for the cloud. Futur. Gener. Comput. Syst. 68, 175–182 (2017)

    Article  Google Scholar 

  18. Kristiani E., Yang C.T., Wang Y.T., Huang C.Y.: Implementation of an Edge Computing Architecture Using Openstack and Kubernetes. In: International Conference on Information Science and Applications pp. 675–685. Springer (2018)

  19. Kumar K., Kurhekar M.: Economically Efficient Virtualization over Cloud Using Docker Containers. In: 2016 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) pp. 95–100. IEEE (2016)

  20. Li H., Zhou H., Zhang H., Feng B., Shi W.: Emustack: an openstack-based dtn network emulation platform (extended version). Mobile Information Systems 2016 Article ID 6540207 15 pages (2016)

  21. Lima S., Rocha Á., Roque L.: An overview of openstack architecture: a message queuing services node. Cluster Computing 22(3) 7087–7098 (2019)

  22. Liu H., An L., Ren J., Wang B.: An interactive traffic replay method in a scaled-down environment. IEEE Access 7 149373–149386 (2019)

  23. Lucas K. byte-unixbench. https://github.com/kdlucas/byte-unixbench

  24. Ma H., Li L., Liang Y., Chen J., Yin J.: Efficient virtual network transmission using correlated equilibrium on xen-based platform. J. Vis. Commun. Image Represent. 35, 248–256 (2016)

    Article  Google Scholar 

  25. Monsalve J., Landwehr A., Taufer M.: Dynamic Cpu Resource Allocation in Containerized Cloud Environments. In: 2015 IEEE International Conference on Cluster Computing pp. 535–536. IEEE (2015)

  26. Morabito R., Kjällman J., Komu M.: Hypervisors Vs. Lightweight Virtualization: a Performance Comparison. In: 2015 IEEE International Conference on Cloud Engineering pp. 386–393. IEEE (2015)

  27. Noel B., Michelino D., Velten M., Rocha R., Trigazis S.: Integrating Containers in the Cern Private Cloud. In: 22nd International Conference on Computing in High Energy and Nuclear Physics pp. 092045. IOP Publishing (2017)

  28. OpenStack: Kolla. https://docs.openstack.org/kolla/latest/

  29. OpenStack: Native open vswitch firewall driver. https://docs.openstack.org/neutron/rocky/admin/config-ovsfwdriver.html

  30. OpenStack: Zun. https://docs.openstack.org/zun/latest/#what-is-zun

  31. Rostanski M., Grochla K., Seman A.: Evaluation of Highly Available and Fault-Tolerant Middleware Clustered Architectures Using Rabbitmq. In: 2014 Federated Conference on Computer Science and Information Systems pp. 879–884. IEEE (2014)

  32. Sahasrabudhe S., Sonawani S.S.: Improved Filter-Weight Algorithm for Utilization-Aware Resource Scheduling in Openstack. In: 2015 International Conference on Information Processing (ICIP) pp. 43–47. IEEE (2015)

  33. Shetty J., Upadhaya S., Rajarajeshwari H., Shobha G., Chandra J.: An empirical performance evaluation of docker container openstack virtual machine and bare metal server. Ind. J. Electr. Eng. Comput. Sci. 7(1), 205–213 (2017)

    Google Scholar 

  34. Tang L., Mars J., Zhang X., Hagmann R., Hundt R., Tune E.: Optimizing Google’s Warehouse Scale Computers: The Numa Experience. In: 2013 IEEE 19th International Symposium on High Performance Computer Architecture (HPCA) pp. 188–197. IEEE (2013)

  35. Tarasov V., Rupprecht L., Skourtis D., Warke A., Hildebrand D., Mohamed M., Mandagere N., Li W., Rangaswami R., Zhao M.: In Search of the Ideal Storage Configuration for Docker Containers. In: 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems (FAS* W) pp. 199–206. IEEE (2017)

  36. Tian Z., Shi W., Wang Y., Zhu C., Du X., Su S., Sun Y., Guizani N.: Real time lateral movement detection based on evidence reasoning network for edge computing environment. IEEE Trans. Ind. Inf. 15(7), 4285–4294 (2019)

    Article  Google Scholar 

  37. Vinoski S.: Advanced message queuing protocol. IEEE Internet Comput. 10(6), 87–89 (2006)

    Article  Google Scholar 

  38. Wang X., Zhai M., Zhang G.: Research on High-Fidelity Router Emulation Technologies Based on Cloud Platform. In: 2018 IEEE 7Th International Conference on Cloud Networking (Cloudnet) pp. 1–4. IEEE (2018)

  39. WiKi: cgroups. https://en.wikipedia.org/wiki/Cgroups

  40. WiKi: Vmware. https://en.wikipedia.org/wiki/VMware

  41. Xavier M.G., Neves M.V., Rossi F.D., Ferreto T.C., Lange T., De Rose C.A.: Performance Evaluation of Container-Based Virtualization for High Performance Computing Environments. In: 2013 21st Euromicro International Conference on Parallel Distributed and Network-Based Processing pp. 233–240. IEEE (2013)

  42. Yamato Y., Nishizawa Y., Nagao S., Sato K.: Fast and reliable restoration method of virtual resources on openstack. IEEE Trans. Cloud Comput. 6(2), 572–583 (2018)

    Article  Google Scholar 

  43. Zhai M., Jiang X., Wang X.: Research on high-throughput routing simulation based on openstack. Computer Engineering and Applications 54(22) 74–79 (2018)

  44. Zhang T., Lee R.B.: Design implementation and verification of cloud architecture for monitoring a virtual machine’s security health. IEEE Trans. Comput. 67(6), 799–815 (2018)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

The work is supported by the National Natural Science Foundation of China (grant nos. 61672264 and 61972182) the National Key R&D Program of China (grant no. 2016YFB0800803) and the Peng Cheng Laboratory Project of Guangdong Province (grant no. PCL2018KP004). An earlier version of this paper was presented at the 4th IEEE International Conference on Data Science in Cyberspace (IEEE DSC).

Author information

Authors and Affiliations

  1. School of Internet of Things Engineering, Jiangnan University, Wuxi, Jiangsu Province, China

    Sijie Yang, Xiaofeng Wang, Xiaoxue Wang & Guizhu Zhang

  2. Cyberspace Security Research Center, Peng Cheng Laboratory, Shenzhen, Guangdong Province, China

    Xiaofeng Wang

  3. School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing, China

    Lun An

Authors
  1. Sijie Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaofeng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaoxue Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lun An
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Guizhu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaofeng Wang.

Ethics declarations

Conflict of interests

The authors declare that they have no conflicts of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article belongs to the Topical Collection: Special Issue on Data Science in Cyberspace 2019

Guest Editors: Bin Zhou, Feifei Li and Jinjun Chen

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, S., Wang, X., Wang, X. et al. High-performance docker integration scheme based on OpenStack. World Wide Web 23, 2593–2632 (2020). https://doi.org/10.1007/s11280-020-00789-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11280-020-00789-9

Keywords

Search

Navigation