Design and Implementation of a Robust Convolutional Neural Network-Based Traffic Matrix Estimator for Cloud Networks

被引:6
|
作者
Memon, Rashida Ali [1 ]
Qazi, Sameer [2 ]
Khan, Bilal Muhammad [1 ]
机构
[1] Natl Univ Sci & Technol, PN Engn Coll, Dept Elect & Power Engn, Habib Ibrahim Rd, Karachi 75350, Pakistan
[2] PAF Karachi Inst Econ & Technol PAF KIET, Coll Engn, Dept Elect Engn, Karachi 75190, Pakistan
来源
WIRELESS COMMUNICATIONS & MOBILE COMPUTING | 2021年 / 2021卷 / 2021期
关键词
TOMOGRAPHY;
D O I
10.1155/2021/1039613
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Recent research literature shows promising results by convolutional neural network- (CNN-) based approaches for estimation of traffic matrix of cloud networks using different architectures. Although conventionally, convolutional neural network-based approaches yield superior estimation; however, these rely on assumptions of availability of a large training dataset which is completely accurate and nonsparse. In real world, both these assumptions are problematic as training data size may be limited, and it is also prone to missing (or incomplete) measurements as well as may have measurement errors. Similarly, the 2-D training datasets derived from network topology based may be sparse. We investigate these challenges and develop a novel architecture which can cater for these challenges and deliver superior performance. Our approach shows promising results for traffic matrix estimation using convolutional neural network-based techniques in the presence of limited training data and outlier measurements.
引用
收藏
页数:11
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