Container Session Level Traffic Prediction From Network Interface Usage

Abstract

Provisioning cloud native services via containers has been regarded as a promising way to promote the cloud elasticity. A container may simultaneously sustain multiple services with a number of different communication sessions. It is of great importance to predict them for fine-grain system management. However, this is a non-trivial task as the session traffics are all invisible. The only thing we can get is the container network interface usage as the total traffic of all coexisting sessions. In this paper, we propose a machine learning based session level traffic prediction framework called X-Rayer, to predict respective session traffics from the network interface usage. Via a sliding-window based ensemble empirical mode decomposition algorithm, X-Rayer first accurately predicts the interface usage, which is then decomposed into session traffics by an invented ConvGRU formed by convolutional neural network and gated recurrent unit. Specially, the spatial-temporal correlations of the interface usages are abstracted via an attention strategy and explored for accurate session traffic decomposition. Through extensive trace-driven experiments, we show that our X-Rayer provides more accurate results by decreasing the average RMSE in the interface usage prediction by 33.25% and 33.71%, and session traffic estimation by 18.05%, 27.04%, 21.91%, and 16.43%, compared to state-of-the-art approaches.