Journal Article

Forecasting short-term data center network traffic load with convolutional neural networks

Mozo, Alberto, Ordozgoiti, Bruno, Gómez-Canaval, Sandra

2018

Overview

Efficient resource management in data centers is of central importance to content service providers as 90 percent of the network traffic is expected to go through them in the coming years. In this context we propose the use of convolutional neural networks (CNNs) to forecast short-term changes in the amount of traffic crossing a data center network. This value is an indicator of virtual machine activity and can be utilized to shape the data center infrastructure accordingly. The behaviour of network traffic at the seconds scale is highly chaotic and therefore traditional time-series-analysis approaches such as ARIMA fail to obtain accurate forecasts. We show that our convolutional neural network approach can exploit the non-linear regularities of network traffic, providing significant improvements with respect to the mean absolute and standard deviation of the data, and outperforming ARIMA by an increasingly significant margin as the forecasting granularity is above the 16-second resolution. In order to increase the accuracy of the forecasting model, we exploit the architecture of the CNNs using multiresolution input distributed among separate channels of the first convolutional layer. We validate our approach with an extensive set of experiments using a data set collected at the core network of an Internet Service Provider over a period of 5 months, totalling 70 days of traffic at the one-second resolution.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Acoustics / Analysis / Artificial intelligence / Artificial neural networks / Biology and Life Sciences / Communications traffic / Computer and Information Sciences / Control / Data centers / Forecasting / Industry forecasts / Information processing / International conferences / Internet service providers / Internet traffic / Model accuracy / Neural networks / Physical Sciences / Research and Analysis Methods / Resource management / Short term / Signal processing / Speech / Time series / Traffic / Traffic management