  One main challenge for the design of networks is that traffic load is not
generally known in advance. This makes it hard to adequately devote resources
such as to best prevent or mitigate bottlenecks. While several authors have
shown how to predict traffic in a coarse grained manner by aggregating flows,
fine grained prediction of traffic at the level of individual flows, including
bursty traffic, is widely considered to be impossible. This paper shows, to the
best of our knowledge, the first approach to fine grained per flow traffic
prediction. In short, we introduce the Frequency-based Kernel Kalman Filter
(FKKF), which predicts individual flows' behavior based on measurements. Our
FKKF relies on the well known Kalman Filter in combination with a kernel to
support the prediction of non linear functions. Furthermore we change the
operating space from time to frequency space. In this space, into which we
transform the input data via a Short-Time Fourier Transform (STFT), the peak
structures of flows can be predicted after gleaning their key characteristics,
with a Principal Component Analysis (PCA), from past and ongoing flows that
stem from the same socket-to-socket connection. We demonstrate the
effectiveness of our approach on popular benchmark traces from a university
data center. Our approach predicts traffic on average across 17 out of 20
groups of flows with an average prediction error of 6.43% around 0.49 (average)
seconds in advance, whilst existing coarse grained approaches exhibit
prediction errors of 77% at best.
