# Investigation of Self-similar Properties of Additive Data Traffic

## TL;DR

This paper numerically investigates the self-similar properties of additive data traffic, revealing how the Hurst exponent of combined streams depends on individual stream characteristics.

## Contribution

It introduces a numerical analysis showing the relationship between the Hurst exponent of total traffic and component streams' properties.

## Key findings

- Hurst exponent of total stream is determined by the maximum Hurst exponent among individual streams.
- The ratio of variation coefficients influences the combined traffic's self-similarity.
- Self-similar properties can be predicted based on component stream parameters.

## Abstract

The work presents results of numerical study of self-similar properties of additive data traffic. It is shown that the value of Hurst exponent of total stream is determined by the maximum value of Hurst exponent of summed streams and the ratio of variation coefficient of stream with maximum Hurst exponent and other ones.

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Source: https://tomesphere.com/paper/1904.05925