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

ABSTRACT: 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 impr

SUBMITTER: Mozo A 

PROVIDER: S-EPMC5800645 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature