@INPROCEEDINGS{Alza2106:OctoMap,
AUTHOR="Aziza Alzadjali and Maria Mushtaq and Flavio Esposito and Claudio Fiandrino
and Jitender Singh Deogun",
TITLE="{OctoMap:} Supporting Service Function Chaining via Supervised Learning and
Online Contextual Bandit",
BOOKTITLE="2021 IEEE 7th International Conference on Network Softwarization (NetSoft)
(NetSoft 2021)",
ADDRESS="Tokyo, Japan",
DAYS=27,
MONTH=jun,
YEAR=2021,
KEYWORDS="Service Function Chaining; Network Function Virtualization; Machine
Learning; Contextual Bandit",
ABSTRACT="Network Function Virtualization (NFV) replaces physical middleboxes with
elastic Virtual Network Functions (VNFs). Those VNFs need to be
instantiated, and their resources dynamically scaled to meet application
and traffic fluctuation requirements. Despite recent extensive research,
deciding how to map virtual resources optimally to the underlying
infrastructure remains practically a challenge. Existing approaches mostly
assign fixed resources to each VNF instance, and transfer virtual flows
using a single physical path, without prior knowledge of traffic patterns
and available bandwidth. Such resource binding strategies lead to
suboptimal physical link utilization. We advance the state of the art in
this regard by presenting OctoMap, a system designed to support with
learning theory any chain embedding algorithm. OctoMap utilizes a
Convolution Neural Network for traffic prediction and provisioning, and a
contextual multi-armed bandit algorithm to solve the online VNF chain
embedding problem. We show the performance benefits of OctoMap with a
trace-driven simulation campaign using publicly available datasets. In
particular, we show how OctoMap reduces the costs of provisioning network
services under node and link constraints, comparing different predictors
and different multi-armed bandit policies."
}