Skip to main content
SpringerLink
Log in

cTMvSDN: improving resource management using combination of Markov-process and TDMA in software-defined networking

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Network simulation and capabilities in the form of a logical network has increased the development of virtual networks rapidly. It is one of the best ways to increase productivity and optimize hardware equipment. Network-Virtualization plays a very important role in the development of networks as the size of the networks increases vastly. This paper examines one of the most important issues in network virtualization to provide an efficient dynamic resources infrastructure management on Software-Based Networks. The proposed method (cTMvSDN) improves resource management based on combination of Markov-Process and Time Division Multiple Access (TDMA) protocol. A customized module to the controller only initializes the mapping when there are sufficient available resources. In order to optimize the response time and SDN Quality of service, the Markov-Pattern and TDMA slicing model are used to predict the next time gaps. Successfully mapped packets will be sent in TDMA slots. Simulation results performed with NS2 and Mininet simulator showed improvement in metrics such as delay and costs in comparison with relevant studies in the literature.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Nguyen V-G, Do T-X, Kim Y (2016) SDN and virtualization-based LTE mobile network architectures: a comprehensive survey. Wireless Pers Commun 86(3):1401–1438

    Article  Google Scholar 

  2. Abuarqoub A (2020) A review of the control plane scalability approaches in software defined networking. Future Internet 12(3):49

    Article  Google Scholar 

  3. Javadpour A, Wang G, Rezaei S (2020) Resource management in a peer to peer cloud network for IoT. Wireless Pers Commun 115(3):2471–2488

    Article  Google Scholar 

  4. Chiha A, Van der Wee M, Colle D, Verbrugge S (2020) Network slicing cost allocation model. J Netw Syst Manag 28(3):627–659

    Article  Google Scholar 

  5. Javadpour A (2020) Providing a way to create balance between reliability and delays in SDN networks by using the appropriate placement of controllers. Wireless Pers Commun 110(2):1057–1071. https://doi.org/10.1007/s11277-019-06773-5

    Article  Google Scholar 

  6. Aglianò S, Ashjaei M, Behnam M, Bello LL (2018) “Resource management and control in virtualized SDN networks”, in 2018. Real-Time Embed Syst Technol (RTEST) 2018:47–53

    Google Scholar 

  7. Singh S, Jha RK (2017) A survey on software defined networking: architecture for next generation network. J Netw Syst Manag 25(2):321–374

    Article  Google Scholar 

  8. Javadpour A, Wang G, Rezaei S, Li KC (2020) Detecting straggler MapReduce tasks in big data processing infrastructure by neural network. J Supercomput 76(9):6969–6993

    Article  Google Scholar 

  9. Javadpour A, Kazemi Abharian S, Wang G (2017) “Feature selection and intrusion detection in cloud environment based on machine learning algorithms. In: 2017 IEEE international symposium on parallel and distributed processing with applications and 2017 IEEE international conference on ubiquitous computing and communications (ISPA/IUCC), pp 1417–1421

  10. Javadpour A, Wang G, Li KC (2019) A high throughput MAC protocol for wireless body area networks in intensive care. In: Smart city and informatization, pp 23–34

  11. Javadpour A (2019) Improving resources management in network virtualization by utilizing a software-based network. Wirel Pers Commun 106(2):505–519

    Article  Google Scholar 

  12. Huang G, Youn HY (2020) Proactive eviction of flow entry for SDN based on hidden Markov model. Front Comput Sci 14(4):144502

    Article  Google Scholar 

  13. Javadpour A, Wang G, Xing X (2018) Managing heterogeneous substrate resources by mapping and visualization based on software-defined network. In: 2018 IEEE Intl Conf on parallel distributed processing with applications, ubiquitous computing communications, big data cloud computing, social computing networking, sustainable computing communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom), 2018, pp 316–321

  14. Tayyaba SK, Shah MA (2019) Resource allocation in SDN based 5G cellular networks. Peer-to-Peer Netw Appl 12(2):514–538

    Article  Google Scholar 

  15. Chen Q, Yu FR, Huang T, Xie R, Liu J, Liu Y (2016) Joint resource allocation for software defined networking, caching and computing. IEEE Glob Commun Conf (GLOBECOM) 2016:1–6

    Google Scholar 

  16. Zhang T, Liu B (2019) Exposing end-to-end delay in software-defined networking. Int J Reconfigurable Comput 2019:7363901

    Article  Google Scholar 

  17. Yi B, Wang X, Huang M, Zhao Y (2020) Novel resource allocation mechanism for SDN-based data center networks. J Netw Comput Appl 155:102554

    Article  Google Scholar 

  18. Amiri M, Al Osman H, Shirmohammadi S (2018) Game-aware and SDN-assisted bandwidth allocation for data center networks. In: 2018 IEEE conference on multimedia information processing and retrieval (MIPR), pp 86–91

  19. Raddwan B, AL-Wagih K, Al-Baltah IA, Alrshah MA, Al-Maqri MA, (2019) Path mapping approach for network function virtualization resource allocation with network function decomposition support. Symmetry (Basel) 11(9):1–25

    Google Scholar 

  20. Mijumbi R, Serrat J, Rubio-Loyola J, Bouten N, Turck FD, Latré S (2014) Dynamic resource management in SDN-based virtualized networks. In: 10th International conference on network and service management (CNSM) and workshop, pp 412–417

  21. Bremaud P (2013) Markov chains: Gibbs fields, Monte Carlo simulation, and queues. Springer, New York

    MATH  Google Scholar 

  22. Ibe O (2005) Fundamentals of applied probability and random processes. Elsevier, Amsterdam

    MATH  Google Scholar 

  23. Malepati H (2010) Digital media processing: dsp algorithms using C. Elsevier, Amsterdam

    MATH  Google Scholar 

  24. Mirmohseni SM, Tang C, Javadpour A (2020) Using Markov learning utilization model for resource allocation in cloud of thing network. Wireless Pers Commun 115(1):653–677. https://doi.org/10.1007/s11277-020-07591-w

    Article  Google Scholar 

  25. Jafari F, Mostafavi S, Mizanian K, Jafari E (2021) An intelligent botnet blocking approach in software defined networks using honeypots. J Ambient Intell Human Comput 12(2):2993–3016

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported in part by the National Natural Science Foundation of China under Grants 61632009 & 61472451, in part by the Guangdong Provincial Natural Science Foundation under Grant 2017A030308006 and High-Level Talents Program of Higher Education in Guangdong Province under Grant 2016ZJ01.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Guangzhou University, Guangzhou, 510006, China

    Amir Javadpour & Guojun Wang

Authors
  1. Amir Javadpour
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guojun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amir Javadpour.

Additional information

Publisher's Note

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

Appendix—Script VNR Virtualizations

Appendix—Script VNR Virtualizations

figure b
figure c
figure d
figure e
figure f

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Javadpour, A., Wang, G. cTMvSDN: improving resource management using combination of Markov-process and TDMA in software-defined networking. J Supercomput 78, 3477–3499 (2022). https://doi.org/10.1007/s11227-021-03871-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-021-03871-9

Keywords

Search

Navigation