Optimized and Secure Multiple Predictions Based Traffic Redundancy Elimination - A Survey

Authors

  • Anto Viji A  M.E Reseach Scholar, CSI Institute of Technology, Thovalai, Tamil Nadu, India
  • Dr.J.Jasper  Ph.D, Department of Electrical and Electronics Engineering, Ponjesly College of Engineering, Nagercoil, Tamil Nadu, India

Keywords:

Cloud Computing, Neural Networks, Traffic redundancy Elimination, Optimization

Abstract

Cloud computing is a specific term that involves delivering hosted services over the Internet, Cloud offers various types of on demand services that enable users to access simultaneous computing capabilities. Traffic Redundancy elimination is the most common factor in cloud computing. Cloud Computing involves mostly based on the concepts of high performance computing. Cloud Storage delivers virtualized storage on demand, over a network based on a request for a given quality of service. Redundancy elimination can be enhanced by mutual perceptive between the sender and receiver. Using either sender-based TRE or receiver-based TRE cannot simultaneously capture traffic redundancy in both short-term) and long-term data redundancy, which concurrently appear in the traffic. Cloud security is a major risk factor in cloud computing. In order to enhance the security and elimination of redundant data can be done by a novel proposed algorithm based on neural network schema.

References

  1. Eyal Zohar, Israel Cidon, and Osnat Mokryn “PACK: Prediction-Based Cloud Bandwidth and Cost Reduction System” IEEE, 2014.
  2. B Agarwal, A. Akella, A. Anand, A. Balachandran, P. Chitnis, C. Muthukrishnan, R. Ramjee, and G. Varghese, “Endre: An end-system redundancy elimination service for enterprises,” in NSDI, 2010, pp. 419– 432.
  3. Jin Li, Yan Kit Li, Xiaofeng Chen, Patrick P. C. Lee, Wenjing Lou “A Hybrid Cloud Approach for Secure Authorized Deduplication” IEEE, 2014.
  4. Lei Yu, Haiying Shen, Karan Sapra, Lin Ye and Zhipeng Cai “CoRE: Cooperative End-to-End TrafficRedundancy Elimination for Reducing Cloud Bandwidth Cost” IEEE, 2016.
  5. Swathi Kurunji, Tingjian Ge, Benyuan Liu, Cindy X. Chen “Communication Cost Optimization for Cloud Data Warehouse Queries” IEEE, 2012.
  6. Lluis Pamies-Juarez, Pedro Garc__a-L_opez, Marc S_anchez-Artigas, Blas Herrera, “Towards the Design of Optimal Data Redundancy Schemes for Heterogeneous Cloud Storage Infrastructures” Computer Networks, 2011.
  7. A Gupta, A. Akella, S. Seshan, S. Shenker, and J. Wang, “Understanding and exploiting network traffic redundancy” UWMadison, Madison, WI, USA, Tech. Rep. 1592, Apr. 2007.
  8. Zhifeng Xiao and Yang Xiao, Senior Member, IEEE, “Security and Privacy in Cloud Computing”, IEEE 2013.
  9. S Ihm, K. Park, and V. Pai. Wide-area Network Acceleration for the Developing World. 2010.
  10. E. Zohar, I. Cidon, and O. O. Mokryn, “The power of prediction: cloud bandwidth and cost reduction,” in ACM SIGCOMM, 2011, pp. 86–97.
  11. N. T. Spring and D. Wetherall, “A protocol-independent technique for eliminating redundant network tra_c,” in ACM SIGCOMM, 2000, pp. 87–95.
  12. A. Anand, C. Muthukrishnan, A. Akella, and R. Ramjee, “Redundancy in network traffic: findings and implications,” in SIGMETRICS /Performance, 2009, pp. 37–48.
  13. L. Breslau, P. Cao, L. Fan, G. Phillips, and S. Shenker. Web caching and zipf-like distributions: Evidence and implications. In IEEE Infocom, 1999.

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Published

2018-06-30

Issue

Section

Research Articles

How to Cite

[1]
Anto Viji A, Dr.J.Jasper, " Optimized and Secure Multiple Predictions Based Traffic Redundancy Elimination - A Survey, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 4, Issue 8, pp.377-382, May-June-2018.