Prediction of Mobile-App Network-Traffic Aggregates using ...
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Prediction of Mobile-App Network-Traffic Aggregates using Multi-task Deep Learning
L. Pappone∗, V. Persico^, D. Ciuonzo^, A. Pescapé^, F. Esposito*
∗Saint Louis University, USA ^University of Naples Federico II, Italy
Input-Output Construction
After collecting traffic generated by mobile
applications, it is submitted to an aggregation module
in order to subdivide it in memory windows, extract
the features of interest and build the input structure
to feed the models.
Consecutive windows always include non-overlapping
sets of packets. Therefore, ∆𝑀 is defined as the
aggregation granularity: each packet of each biflow
that occurs within this time is properly aggregated
according to the specific feature of interest.
As a result of the process, for each ∆𝑀 a single
aggregate value is generated for each feature, so the
resulting time-series will be generated for the single
biflow.
Our single-step prediction approach is based on an
incremental windowing procedure for input-output
construction: when approaching to the windowing,
incrementally-sized sets of samples are grouped
together until reaching the prescribed maximum size
W of the prediction window (i.e., predictions can be
made as soon as the first sample is available). That
leads to model the border effects, (i.e., the early
behaviors), which are achieved through a left zero-
padding up to W samples.
Experimental Analysis
Modeling and predicting network traffic have
become essential to many diverse practical
contexts, ranging from security and network
planning to QoS optimization. Nonetheless,
modeling such processes is challenging due to the
complexity and variability of the network traffic.
Several modeling approaches have been
proposed to improve classification and forecasting
performances, spanning different objectives,
excluding the prediction of aggregated network
traffic generated by mobile applications.
In this poster, we aim at filling this gap by
exploring the suitability of several Deep learning
(DL) and Machine Learning (ML) models to such
aim. In particular, we used multi-task deep
learning to predict several aggregate traffic
features, including downstream and upstream
traffic volumes, on different window sizes, showing
promising performances.
Abstract
[1] G. Aceto et al. MIRAGE: Mobile-app Traffic Capture and
Ground-truth Creation. In 2019 IEEE ICCCS.
[2] W. Wang el al., “End-to-end encrypted traffic
classification with one-dimensional convolution neural
networks,” in IEEE International Conference on Intelligence
and Security Informatics (ISI), 2017.
[3] J. Chung et al., “Empirical evaluation of gated recurrent
neural networks on sequence modeling,”arXiv
preprintarXiv:1412.3555, 2014.
[4] M. Barabas et al., “Evaluation of network traffic
prediction based on neural networks with multi-task learning
and multiresolution decomposition,” in IEEE 7° International
Conference on Intelligent Computer Communication and
Processing (ICCP), 2011, pp. 95–102.
We analyze the suitability of state-of-art DL/ML models:
• Convolutional Neural Network (CNN) [2],
• Gated Recurrent Unit (GRU) [3]
• Random Forest Regressor (RFR)
to predict mobile-network traffic aggregates.
We predict four aggregated traffic features, namely
upstream traffic volume, downstream traffic volume,
upstream No. of packets, downstream No. of packets.
We evaluate DL models compared with a ML baseline -
extracted from video applications [1] bi-directional flows
traffic traces – relying on short-term aggregation windows
(in the order of milliseconds).
Single-Step Prediction
References
Acknowledgement*The work of Lorenzo Pappone and Flavio Esposito has been partially supported by NSF # 1836906 and # 1908574.
Deep Learning Architectures
We leverage Multi-Task learning in order to tackle the
multivariate nature of the prediction task.
Multi-task learning consists in solving multiple prediction
problems, each associated to an aggregated traffic
feature [4]. It is implemented sharing a single DL
architecture (CNN or GRU) among different problems,
thus constituting a significant difference in terms of
computational complexity with respect to a multiple
single-task learning approach.
Multi-Task Learning
Trend Performance Analysis
GRU
CNN
Experimental Results
In this work, our contribution can be summarized as follows:
• We formally define the traffic aggregates prediction problem and related parameters;
• We report experimental results concerning the comparison between state-of-art DL architectures in terms of
prediction performances along with a ML baseline;
• We design systematic pre-processing framework to combine different aggregation windows (revealing potential
traffic patterns over shorter time period), employing an input-output construction process to feed the models.
• We provide a trend performance analysis regarding the application of smaller aggregation windows to predict
larger time periods.
Our Contribution
Features of interest
Input-Output
construction