@INPROCEEDINGS{Scal1906:Scalable,
AUTHOR="Alessio Scalingi and Flavio Esposito and Waqar Muhammad and Antonio
Pescap{\'e}",
TITLE="Scalable Provisioning of Virtual Network Functions via Supervised Learning",
BOOKTITLE="2019 IEEE Conference on Network Softwarization (NetSoft) (NetSoft 2019)",
ADDRESS="Paris, France",
DAYS=23,
MONTH=jun,
YEAR=2019,
KEYWORDS="NFV; Supervised Learning; Time Series Analysis; Deep Learning",
ABSTRACT="Network Function Virtualization (NFV) is opening new opportunities for both
the business and the research community. As the need to softwarize
functions grows, managing the underlying hosting infrastructure faces new
challenges. In this paper, we focus on one of these challenges: the ability
to provision enough virtualizable infrastructure resources to guarantee
smooth and responsive network and application operations. To this aim, we
learn from observed patterns of requests to an infrastructure hosting
virtual network functions, and we model the problem of appropriately
scaling resources to provision them. Using months of real Internet traffic
requests, we train and compare the performance of several (classical and
more recent) learning algorithms. Our goal is to predict future NFV
requests to proactively provision our infrastructure using constraint
optimization. Our results, obtained with simulations and with a prototype
that deploys Linux containers, show both expected and surprising results,
and aims at fostering debates on when and if the juice of (supervised) deep
learning techniques is worth the squeeze."
}