Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Short-Term Flow-Based Bandwidth Forecasting using Machine Learning   This paper proposes a novel framework to predict traffic flows' bandwidth
ahead of time. Modern network management systems share a common issue: the
network situation evolves between the moment the decision is made and the
moment when actions (countermeasures) are applied. This framework converts
packets from real-life traffic into flows containing relevant features. Machine
learning models, including Decision Tree, Random Forest, XGBoost, and Deep
Neural Network, are trained on these data to predict the bandwidth at the next
time instance for every flow. Predictions can be fed to the management system
instead of current flows bandwidth in order to take decisions on a more
accurate network state. Experiments were performed on 981,774 flows and 15
different time windows (from 0.03s to 4s). They show that the Random Forest is
the best performing and most reliable model, with a predictive performance
consistently better than relying on the current bandwidth (+19.73% in mean
absolute error and +18.00% in root mean square error). Experimental results
indicate that this framework can help network management systems to take more
informed decisions using a predicted network state.

8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
15
The categories of this paper are: nucl-ex
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
14
The categories of this paper are: math-ph