Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
SpringerLink
Log in

Scalable Models for Redundant Data Flow Analysis in Online Social Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Sharing and reception of identical video contents amongst the users of various online social media applications induce enormous amount of redundant data traffic flow across Internet layer, various planes of Service provider’s network and also increase in the data consumption of the end users. The primary aim of this research is to reduce the routing of redundant data across social media by means of a novel procedure based on the validation of pre-existence of contents at the receiver’s end prior to the actual video content transmission. A proactive middleware is proposed to handle the enormous growth of social media traffic transparently and hence a distributed Web based framework has been implemented for real-time traffic flow analysis. As part of the Service Oriented Architectural model, various services are defined to fetch a frame from each initiated video content, to validate the pre-existence at receiver’s end and to take appropriate decision for acceptance or deferral of the actual video transfer. The middleware which has been developed for redundant data flow traffic analysis is further extended to cloud services to address the scalability issues. The statistical analysis carried out by the proposed models depicted 27% of reduction in the total initiated traffic with adequate amount of data saving for the end users. The overhead associated in the transmission of the first frame of the actual video is also analyzed and it is observed that overhead is led by the substantial amount of gains achieved by the proposed model.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 9
Fig. 10

Similar content being viewed by others

Explore related subjects

Find the latest articles, discoveries, and news in related topics.

References

  1. Anand, A., Gupta, A., Akella, A., Seshan, S., & Shenker, S. (2008). Packet caches on routers: The implications of universal redundant traffic elimination. In Proceedings of the ACM SIGCOMM 2008 conference on data communication, Seattle, WA, USA (pp. 219–230).

  2. Yamamoto, S., & Nakao, A. (2012). P2P packet cache router for network-wide traffic redundancy elimination. In Proceedings of the 2012 international conference on computing, networking and communications (ICNC), Maui, HI (pp. 830–834).

  3. Spring, N. T., & Wetherall, D. (2000). A protocol-independent technique for eliminating redundant network traffic. In Proceedings of the ACM SIGCOMM’00, Stockholm, Sweden (pp. 87–95).

  4. Halepovic, E., Williamson, C., & Ghaderi, M. (2012). Enhancing redundant network traffic elimination. Computer Networks, 56(2), 795–809.

    Article  Google Scholar 

  5. Yokota, W., Ichijo, K., & Narita, A. (2016). Evaluation of the redundant traffic reduction node using the packet cache coping with different byte offsets among streams. In Proceedings of the 2016 International Conference on Networking and Network Applications (NaNA), Hakodate, Japan (pp. 227–232).

  6. Zeydan, E., Bastug, E., Bennis, M., Kader, M. A., Karatepe, I. A., Er, A. S., et al. (2016). Big data caching for networking: moving from cloud to edge. IEEE Communications Magazine, 54(9), 36–42.

    Article  Google Scholar 

  7. Wang, X., Li, X., Leung, V. C. M., & Nasiopoulos, P. (2015). A framework of cooperative cell caching for the future mobile networks. In Proceedings of the 48th Hawaii international conference on system sciences, IEEE Computer Society, Kauai, HI, USA (pp. 5404–5413).

  8. Bastug, E., Bennis, M., & Debbah, M. (2014). Living on the edge: The role of proactive caching in 5G wireless networks. IEEE Communications Magazine, 52(8), 82–89.

    Article  Google Scholar 

  9. Wang, X., Taleb, T., Ksentini, A., & Leung, V. C. M. (2014). Cache in the air: Exploiting content caching and delivery techniques for 5G systems. IEEE Communications Magazine, 52(2), 131–139.

    Article  Google Scholar 

  10. Nagaraju, B., Mariappan, V., & Ramachandran, V. (2018). An effective simulation model for optimal traffic flow across packet data network. International Journal of Applied Engineering Research, 13(9), 7168–7177.

    Google Scholar 

  11. Gomathi, V., & Ramachandran, V. (2010). Service oriented architectural model for power system state estimation. Journal of High Performance Computing, 29(2), 43–49.

    Google Scholar 

  12. Nickul, D., Reitman, L., Ward, J., & Wilber, J. (2007). Service oriented architecture (SOA) and specialized messaging patterns. Technical White Paper.

  13. Django Web Framework. Django Software Foundation. Django Documentation. https://media.readthedocs.org/pdf/django/latest/django.pdf. Accessed April 25, 2018.

  14. Nagaraju, B., Ramachandran, V., & Mariappan, V. (2018). Cloud based simulation model for traffic flow optimization in online social networks. International Journal of Simulation Systems, Science & Technology, 19(2), 8.1–8.12.

    Google Scholar 

  15. Google Cloud. Running Django on App Engine Standard Environment. https://cloud.google.com/python/django/appengine. Accessed March 14, 2018.

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Nagaland, Chumukedima, Dimapur, Nagaland, 797 103, India

    Nagaraju Baydeti

  2. Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, 600062, India

    Ramachandran Veilumuthu

  3. Amazon, Bengaluru, 560103, India

    Mariappan Vaithilingam

Authors
  1. Nagaraju Baydeti
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ramachandran Veilumuthu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mariappan Vaithilingam
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nagaraju Baydeti.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Baydeti, N., Veilumuthu, R. & Vaithilingam, M. Scalable Models for Redundant Data Flow Analysis in Online Social Networks. Wireless Pers Commun 107, 2123–2142 (2019). https://doi.org/10.1007/s11277-019-06375-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-019-06375-1

Keywords

Search

Navigation