Adaptive Linear Prediction Augmented Autoregressive Integrated Moving Average Based Prediction for VBR Video Traffic

Raghuveera Raghuveera

doi:10.3844/jcssp.2011.871.876

Abstract

Problem statement: Network traffic prediction plays a vital role in the optimal resource allocation and management in computer networks. This study introduces an ARIMA based model augmented by Adaptive Linear Prediction (ALP) for the real time prediction of VBR video traffic. The synergy of the two can successfully address the challenges in traffic prediction such as accuracy in prediction, resource management and utilization. Approach: ARIMA application on a VBR video trace results in a component wise representation of the trace which is then used for prediction. The ALP applied afterwards, ensures consistency in error fluctuation and better accuracy in turn. Results: Performance evaluation of the proposed method is carried out using RMSE. The prediction accuracy is improved by 23% and the error variance is reduced by 23%. Conclusion: The performance of the proposed method is thoroughly investigated, by applying it on video traces of different qualities and characteristics.

Highlights

Traffic Prediction is the process of predicting future network traffic based on the characteristics of the past traffic
If the resources are allocated according to the peak rate of the video traffic, no packet loss occurs, but a substantial amount of the resources are wasted during transmission
An Autoregressive Integrated Moving Average (ARIMA) based mechanism augmented by Adaptive Linear Prediction (ALP) for the prediction of VBR video traffic was devised and implemented

Summary

INTRODUCTION

Traffic Prediction is the process of predicting future network traffic based on the characteristics of the past traffic. ARIMA (Autoregressive Integrated Moving Average) is a statistical methodology in time series analysis which is used in the forecast or prediction of future terms based on the characteristics of the past terms. During the process of updating of weight vector the generic ALP, above suggests a parameter called μ, which indicates the step size and would be a fixed value. It has been verified in (Zhao et al, 2002) that the usage of a variable step size for the prediction of each value could result in better prediction accuracy. Μ obtained using Equation (4), if ek+1 < T obtained using Algorithm (1) otherwise

MATERIALS AND METHODS

AND DISCUSSION

Findings

CONCLUSION