TY - GEN
T1 - OctoMap
T2 - 7th IEEE International Conference on Network Softwarization, NetSoft 2021
AU - Alzadjali, Aziza
AU - Mushtaq, Maria
AU - Esposito, Flavio
AU - Fiandrino, Claudio
AU - Deogun, Jitender
N1 - Funding Information:
This work has been supported by NSF under Award Numbers CNS1647084, CNS1836906, and CNS1908574, and by an international travel grant from the GENI Project Office and Boston University, under NSF collaborative agreement CNS-1536090. The work of Aziza Alzadjali was conducted while at Saint Louis University. Dr. Fiandrino's work is supported by the Juan de la Cierva grant (IJC2019-039885-I)
Funding Information:
This work has been supported by NSF under Award Numbers CNS1647084, CNS1836906, and CNS1908574, and by an international travel grant from the GENI Project Office and Boston University, under NSF collaborative agreement CNS-1536090. The work of Aziza Alzadjali was conducted while at Saint Louis University. Dr. Fiandrino’s work is supported by the Juan de la Cierva grant (IJC2019-039885-I).
Publisher Copyright:
© 2021 IEEE.
PY - 2021/6/28
Y1 - 2021/6/28
N2 - 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.
AB - 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.
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U2 - 10.1109/NetSoft51509.2021.9492660
DO - 10.1109/NetSoft51509.2021.9492660
M3 - Conference contribution
AN - SCOPUS:85112049050
T3 - Proceedings of the 2021 IEEE Conference on Network Softwarization: Accelerating Network Softwarization in the Cognitive Age, NetSoft 2021
SP - 133
EP - 141
BT - Proceedings of the 2021 IEEE Conference on Network Softwarization
A2 - Shiomoto, Kohei
A2 - Kim, Young-Tak
A2 - Rothenberg, Christian Esteve
A2 - Martini, Barbara
A2 - Oki, Eiji
A2 - Choi, Baek-Young
A2 - Kamiyama, Noriaki
A2 - Secci, Stefano
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 28 June 2021 through 2 July 2021
ER -