Generalized Self-Similar First Order Autoregressive Generator (GSFO-ARG) for Internet Traffic

Jumoke Popoola,Waheed Babatunde Yahya,Oyebayo Ridwan Olaniran,Olusogo Popoola

doi:10.19139/soic-2310-5070-926

Jumoke Popoola, Waheed Babatunde Yahya + Show 2 more

Open Access

https://doi.org/10.19139/soic-2310-5070-926

Copy DOI

Abstract

Internet traffic data such as the number of transmitted packets and time spent on the transmission of Internet protocols (IPs) have been shown to exhibit self-similar property which can contain the long memory property, particularly in a heavy Internet traffic. Simulating this type of dataset is an important aspect of delay avoidance planning, especially when trying to mimic real-life processing of packets on the Internet. Most of the existing procedures often assumed the process follows a Gaussian distribution, and thus long memory processes such as Fractional Brownian Motion (FBM) and Fractional Gaussian Noise (FGN) among others are used. These approaches often result in estimation errors arising from the use of inappropriate distribution. However, it has been established that the distribution of Internet processes are heavy-tailed. Therefore, in this paper, a new method that is capable of generating heavy-tailed self-similar traffic is proposed based on the first-order autoregressive AR (1) process. The proposed method is compared with some of the existing methods at varying values of the self-similar index and sample sizes. The imposed self-similarity indices were estimated using the Range/Standard deviation statistic (R/S). Performance analysis was achieved using the absolute percentage errors. The results showed that the proposed method has a lower average error when compared with other competing methods.

Highlights

Scientic experimentation has gained a powerful tool with the advent of computers
As a matter of retrospective, we investigated some papers that employed simulated Internet data in the form of voice, text, video, or their combinations to carry out various research on Internet traffic
We present the results of some five existing self-similar generators compared with the proposed GSFO-ARG method at different values of the Hurst parameter H

Summary

Introduction

Scientic experimentation has gained a powerful tool with the advent of computers Nowadays, their immense processing capabilities are used to perform complex simulations of data in various real-world situations. It is known to be embedded in many of the processes relating to natural and articial events [9], with its popularity that may be traced to the ndings that unfold the so-called self-similar nature of Internet traffic. This has brought attention to the fact that the behaviour of trafc in the network aggregation points should be understood by considering the self-similar and non-memoryless property.

Methods

Results

Discussion

Conclusion