Skip to main content

Big Data Analytics for Network Congestion Management Using Flow-Based Analysis

  • Conference paper
  • First Online:
Artificial Intelligence and Evolutionary Computations in Engineering Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 394))

  • 2590 Accesses

Abstract

Due to explosive growth of traffic volume, it is hard to accumulate Internet traffic on a single machine. In this paper, a Hadoop-based traffic analysis system accepts input from multiple data traces. Hadoop facilitates scalable data processing and storage services on a distributed computing system. This system accepts input of large scales of trace file generated from traffic measurement tool like Wireshark– identifies flows running on the network from this trace file. Characteristics of flow describe the pattern of network traffic; it helps network operator understand network capacity planning, traffic engineering, and fault handling. The main objective is to design and implement a traffic flow identification system using Hadoop. The traffic flow identification system will be very useful for network administrator to monitor faults and also to plan for the future.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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. Lee Y. Toward scalable internet traffic measurement and analysis with hadoop, ACM SIGCOMM computer communication review, vol. 43;2013, p. 5–13.

    Google Scholar 

  2. Lee Y, Kang W. A hadoop-based packet trace processing tool. In: Traffic monitoring and analysis. Springer;2011, p. 51–63.

    Google Scholar 

  3. Lee Y, Kang W, Son H. An internet traffic analysis method with mapreduce. In: Network operations and management symposium workshops;2010. p. 357–61.

    Google Scholar 

  4. Qian L, Wu B, Zhang RW. Characterization of 3g data-plane traffic and application towards centralized control and management for software defined networking. In: Big data (big data congress);2013. p. 278–85.

    Google Scholar 

Download references

Acknowledgments

The author wish to thank the guide for their moral support.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yasmeen Arafath .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer India

About this paper

Cite this paper

Arafath, Y., Ranjith Kumar, R. (2016). Big Data Analytics for Network Congestion Management Using Flow-Based Analysis. In: Dash, S., Bhaskar, M., Panigrahi, B., Das, S. (eds) Artificial Intelligence and Evolutionary Computations in Engineering Systems. Advances in Intelligent Systems and Computing, vol 394. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2656-7_41

Download citation

  • DOI: https://doi.org/10.1007/978-81-322-2656-7_41

  • Published:

  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2654-3

  • Online ISBN: 978-81-322-2656-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics