Narasimhan, Harikrishna and Tripuraribhatla, Raghuveera and Easwarakumar, KS (2011) Fuzzy inference based data preprocessing for VBR video traffic prediction. In: 2010 IEEE 4th International Conference on Internet Multimedia Services Architecture and Application(IMSAA), 15-17 Dec. 2010, Bangalore.
Fuzzy_Inference.pdf - Published Version
Restricted to Registered users only
Download (337Kb) | Request a copy
Prediction of variable bit rate compressed video traffic is critical to dynamic allocation of resources in a network. In this paper, we propose a technique for preprocessing the dataset used for training a video traffic predictor. The technique involves identifying the noisy instances in the data using a fuzzy inference system. We focus on three prediction techniques, namely, linear regression, neural network and support vector regression and analyze their performance on H.264 video traces. Our experimental results reveal that data preprocessing greatly improves the performance of linear regression and neural network, but is not effective on support vector regression.
|Item Type:||Conference Paper|
|Additional Information:||Copyright 2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Keywords:||traffic prediction;fuzzy inference system;linear regression; neural network;support vector regression;H.264|
|Department/Centre:||Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)|
|Date Deposited:||29 Dec 2011 04:41|
|Last Modified:||29 Dec 2011 04:41|
Actions (login required)