| 1. |
Characteristics of Internet Traffic Data
The Internet traffic data have been found to possess extreme variability and bursty structures in a wide range of time-scales, so that there is no definite period of busy or silent periods. However, there is a self-similar feature which makes it possible to characterize the data. Self-similarity is expressed in terms of the different statistics varying with the time scale of observation. We give a brief description of those we have calculated to determine the self-similarity of the Interrnet traffic data obtained in our laboratory 〓. These are i)Variance, the decrease of which with the time scale of observation gives a parameter (β) to specify the degree of self-similarity, ii)Autocorrelation, with a very slow decay rate and itself showing self-similar features and iii)Hurst parameter H, another independent measure from the rescaled range of the data. The similarity of the data in a sub-period and its finer intervals leads to the possibility of the data to posses fractal characteristics also. Although extensive works have been done on the self-similar features of Internet traffic data, there has not been much on this aspect, which can exist in both the time and space scales. Here we attempt to provide a description of the fractal characteristics associated with such a self-similarity.. |
| 2. |
Ashir Ahmed, Fumihiko Yokota, Mariko Nishikitani, Kimiyo Kikuchi, Rafiqul Islam, Hasib Rashid, Impact Report on the 3rd International Conference on Healthcare, SDGs and Social Business, 2020.06. |
