Skip to main content
SpringerLink
Log in

An Elephant Flow Detection Method Based on Machine Learning

  • Conference paper
  • First Online:
Smart Computing and Communication (SmartCom 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11910))

Included in the following conference series:

Abstract

Software-Defined Networking (SDN) is regarded as the next generation network. Current network is difficult to be configured and managed, and SDN is proposed to change this situation, which makes it attract a lot of attention of the academia and industry. The detection of Elephant Flow is an important service of SDN, based on which we can achieve the management of the network traffic and implement services such as the load balancing of traffic, congestion avoidance and so on. This paper focuses on the iterative method to detect Elephant Flow. We propose a method which uses the random forest to learn the arguments produced in the iterative detection and to improve the accuracy and speed of the detection. The experiments show that our method can efficiently improve the accuracy and speed of the detection compared to other methods.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Akyildiz, I.F., Lee, A., Wang, P., et al.: A roadmap for traffic engineering in SDN-OpenFlow networks. Comput. Netw. 71, 1–30 (2014)

    Article  Google Scholar 

  2. Thottan, M., Ji, C.: Anomaly detection in IP networks. IEEE Trans. Signal Process. 51(8), 2191–2204 (2003)

    Article  Google Scholar 

  3. Jose, L., Yu, M., Rexford, J.: Online measurement of large traffic aggregates on commodity switches. In: Hot Topics in Management of Internet, Cloud, and Enterprise Networks and Services. USENIX Association (2011)

    Google Scholar 

  4. Qiu, H., Noura, H., Qiu, M., et al.: A user-centric data protection method for cloud storage based on invertible DWT. IEEE Trans. Cloud Comput. 1 (2019)

    Google Scholar 

  5. Gai, K., Qiu, M., Zhao, H., et al.: Dynamic energy-aware cloudlet-based mobile cloud computing model for green computing. J. Netw. Comput. Appl. 59(C), 46–54 (2016)

    Article  Google Scholar 

  6. Gai, K., Qiu, M., Zhao, H.: Energy-aware task assignment for mobile cyber-enabled applications in heterogeneous cloud computing. J. Parallel Distrib. Comput. S0743731517302319 (2017)

    Google Scholar 

  7. Gai, K., Xu, K., Lu, Z., et al.: Fusion of cognitive wireless networks and edge computing. IEEE Wirel. Commun. 26(3), 69–75 (2019)

    Article  Google Scholar 

  8. Alizadeh, M., Edsall, T., Dharmapurikar, S., et al.: CONGA: distributed congestion-aware load balancing for datacenters. ACM SIGCOMM Comput. Commun. Rev. 44(4), 503–514 (2014)

    Article  Google Scholar 

  9. Alizadeh, M., Yang, S., Sharif, M., et al.: pFabric: minimal near-optimal datacenter transport. ACM SIGCOMM Comput. Commun. Rev. 43(4), 435–446 (2013)

    Article  Google Scholar 

  10. Benson, T., Anand, A., Akella, A., et al.: MicroTE: fine grained traffic engineering for data centers. In: Proceedings of the Seventh COnference on Emerging Networking EXperiments and Technologies, p. 8. ACM (2011)

    Google Scholar 

  11. García-Teodoro, P., Díaz-Verdejo, J., Maciá-Fernández, G., et al.: Anomaly-based network intrusion detection: techniques, systems and challenges. Comput. Secur. 28(1–2), 18–28 (2009)

    Article  Google Scholar 

  12. Kabbani, A., Alizadeh, M., Yasuda, M., et al.: AF-QCN: approximate fairness with quantized congestion notification for multi-tenanted data centers. In: 2010 18th IEEE Symposium on High Performance Interconnects, pp. 58–65, 2191–2204. IEEE (2010)

    Google Scholar 

  13. Duffield, N.: Sampling for passive internet measurement: a review. Stat. Sci. 19(3), 472–498, 2191–2204 (2004)

    Article  MathSciNet  Google Scholar 

  14. Cormode, G., Muthukrishnan, S.: An improved data stream summary: the count-min sketch and its applications. J. Algorithms 55(1), 58–75 (2005)

    Article  MathSciNet  Google Scholar 

  15. Moshref, M., Yu, M., Govindan, R., et al.: DREAM: dynamic resource allocation for software-defined measurement. ACM SIGCOMM Comput. Commun. Rev. 44(4), 419–430 (2015)

    Article  Google Scholar 

  16. The caida ucsd anonymized internet traces 2015[EB/OL], 06 April 2019. http://www.caida.org/data/passive/passive_2015_dataset.xml

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Technology, Jilin University, Changchun, China

    Kaihao Lou & Yongjian Yang

  2. Changchun Polytechnic University, Changchun, China

    Chuncai Wang

Authors
  1. Kaihao Lou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yongjian Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chuncai Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chuncai Wang .

Editor information

Editors and Affiliations

  1. Columbia University, New York, NY, USA

    Meikang Qiu

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lou, K., Yang, Y., Wang, C. (2019). An Elephant Flow Detection Method Based on Machine Learning. In: Qiu, M. (eds) Smart Computing and Communication. SmartCom 2019. Lecture Notes in Computer Science(), vol 11910. Springer, Cham. https://doi.org/10.1007/978-3-030-34139-8_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-34139-8_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34138-1

  • Online ISBN: 978-3-030-34139-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation