@INPROCEEDINGS{Espo2301:Hide,
AUTHOR="Alessio Sacco and Flavio Esposito and Guido Marchetto",
TITLE="Hide \& Seek: Traffic Matrix Completion and Inference Using Hidden
Information",
BOOKTITLE="2023 IEEE 20th Consumer Communications \& Networking Conference (CCNC)
(CCNC 2023)",
ADDRESS="Las Vegas, USA",
DAYS="8",
MONTH=jan,
YEAR=2023,
KEYWORDS="matrix completion; measurement; hidden markov model; Adversarial
AutoEncoder",
ABSTRACT="Traffic matrices are used for many network management operations, from
planning to repairing. Despite years of research on the topic, their
estimation and inference on the Internet are still challenging and
error-prone. For example, missing values are unavoidable due to flaws in
the measurement systems and possible failure in data collection systems. It
is thus helpful for many network operators to recover the missing data from
the partial direct measurements. Some existing matrix completion methods do
not fully consider network traffic behavior and hidden traffic
characteristics, showing the inability to adapt to multiple scenarios.
Others instead make assumptions on the matrix structure that may be invalid
or impractical, curtailing the applicability. In this paper, we propose
Hide \& Seek, a novel matrix completion and prediction algorithm based on a
combination of generative autoencoders and Hidden Markov Models. We
demonstrate with an extensive experimental evaluation on real-world
datasets how our algorithm can accurately reconstruct missing values while
predicting their short-term evolution."
}