Details

Autor(en) / Beteiligte

• Memon, Rashida Ali
• Qazi, Sameer
• Khan, Bilal Muhammad
• Qureshi, Nawab Muhammad Faseeh
• Nawab Muhammad Faseeh Qureshi

Titel

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

Ist Teil von

• Wireless communications and mobile computing, 2021, Vol.2021 (1)

Ort / Verlag

Oxford: Hindawi

Erscheinungsjahr

2021

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• 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.