TY - GEN
T1 - HYPER-VINES
T2 - 2019 International Conference on Computing, Networking and Communications, ICNC 2019
AU - Gupta, Lav
AU - Salman, Tara
AU - Das, Ria
AU - Erbad, Aiman
AU - Jain, Raj
AU - Samaka, Mohammed
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/4/8
Y1 - 2019/4/8
N2 - Fault and performance management systems, in the traditional carrier networks, are based on rule-based diagnostics that correlate alarms and other markers to detect and localize faults and performance issues. As carriers move to Virtual Network Services, based on Network Function Virtualization and multi-cloud deployments, the traditional methods fail to deliver because of the intangibility of the constituent Virtual Network Functions and increased complexity of the resulting architecture. In this paper, we propose a framework, called HYPER-VINES, that interfaces with various management platforms involved to process markers through a system of shallow and deep machine learning models. It then detects and localizes manifested and impending fault and performance issues. Our experiments validate the functionality and feasibility of the framework in terms of accurate detection and localization of such issues and unambiguous prediction of impending issues. Simulations with real network fault datasets show the effectiveness of its architecture in large networks.
AB - Fault and performance management systems, in the traditional carrier networks, are based on rule-based diagnostics that correlate alarms and other markers to detect and localize faults and performance issues. As carriers move to Virtual Network Services, based on Network Function Virtualization and multi-cloud deployments, the traditional methods fail to deliver because of the intangibility of the constituent Virtual Network Functions and increased complexity of the resulting architecture. In this paper, we propose a framework, called HYPER-VINES, that interfaces with various management platforms involved to process markers through a system of shallow and deep machine learning models. It then detects and localizes manifested and impending fault and performance issues. Our experiments validate the functionality and feasibility of the framework in terms of accurate detection and localization of such issues and unambiguous prediction of impending issues. Simulations with real network fault datasets show the effectiveness of its architecture in large networks.
KW - Deep Learning
KW - FCAPS
KW - Fault Management
KW - Machine Learning
KW - Multi-Cloud Environments
KW - Network Function Virtualization
KW - Performance Management
KW - Service Function Chain
KW - Virtual Network Function
KW - Virtual Network Service
UR - https://www.scopus.com/pages/publications/85064970037
U2 - 10.1109/ICCNC.2019.8685496
DO - 10.1109/ICCNC.2019.8685496
M3 - Conference contribution
AN - SCOPUS:85064970037
T3 - 2019 International Conference on Computing, Networking and Communications, ICNC 2019
SP - 141
EP - 147
BT - 2019 International Conference on Computing, Networking and Communications, ICNC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 18 February 2019 through 21 February 2019
ER -