Author(s)

Pushpalatha Sarla, Mallikarjuna Reddy Doodipala^*, Manohar Dingari

Department of Engineering Mathematics, GITAM University Hyderabad Campus, Hyderabad, India.

The paper focuses on measuring self-similarity using few techniques by an index called Hurst index which is a self-similarity parameter. It has been evident that Internet traffic exhibits self-similarity. Motivated by this fact, real time web users at various centers considered here as traffic and it has been examined by various methods to test the self-similarity. The results from the experiments carried out verify that the traffic examined in the present study is self similar using a new method based on some descriptive measures; for example percentiles have been applied to compute Hurst parameter which gives intensity of the self-similarity. Numerical results and analysis we discussed and presented here play a significant role to improve the services at web centers in the view of quality of service (QOS).

Long-Range Dependence, Self-Similarity, Poisson Process, Percentiles, Hurst Parameter

Sarla, P. , Doodipala, M. and Dingari, M. (2016) Self Similarity Analysis of Web Users Arrival Pattern at Selected Web Centers. American Journal of Computational Mathematics, 6, 17-22. doi: 10.4236/ajcm.2016.61002.