TY - GEN
T1 - Environment Modeling and Abstraction of Network States for Cognitive Functions
AU - Mwanje, Stephen S.
AU - Kajo, Marton
AU - Majumdar, Sayantini
AU - Carle, Georg
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/4
Y1 - 2020/4
N2 - Cognitive Autonomous Networks (CANs) promise to overcome the shortcomings of current Self-Organizing Network (SON) implementations, i.e., the limited flexibility and adaptability to changing environments, by applying cognition. In CAN, intelligent network automation functions, herein called Cognitive Functions (CFs), apply machine learning techniques to learn context-specific behavioral policies with which to automate network operations. For proper operation, the CAN system needs to learn the environment in which the functions are operating and to abstract the environment and performance observations into states to which the CFs must respond. This paper proposes a design and implementation of an Environmental-state Modeling and Abstraction (EMA) engine that could be tasked to learn the required abstract states in a consistent way across multiple CFs.
AB - Cognitive Autonomous Networks (CANs) promise to overcome the shortcomings of current Self-Organizing Network (SON) implementations, i.e., the limited flexibility and adaptability to changing environments, by applying cognition. In CAN, intelligent network automation functions, herein called Cognitive Functions (CFs), apply machine learning techniques to learn context-specific behavioral policies with which to automate network operations. For proper operation, the CAN system needs to learn the environment in which the functions are operating and to abstract the environment and performance observations into states to which the CFs must respond. This paper proposes a design and implementation of an Environmental-state Modeling and Abstraction (EMA) engine that could be tasked to learn the required abstract states in a consistent way across multiple CFs.
KW - Cognitive Autonomous Network
KW - Cognitive Network Management
KW - Network State Modeling
UR - http://www.scopus.com/inward/record.url?scp=85086761182&partnerID=8YFLogxK
U2 - 10.1109/NOMS47738.2020.9110333
DO - 10.1109/NOMS47738.2020.9110333
M3 - Conference contribution
AN - SCOPUS:85086761182
T3 - Proceedings of IEEE/IFIP Network Operations and Management Symposium 2020: Management in the Age of Softwarization and Artificial Intelligence, NOMS 2020
BT - Proceedings of IEEE/IFIP Network Operations and Management Symposium 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE/IFIP Network Operations and Management Symposium, NOMS 2020
Y2 - 20 April 2020 through 24 April 2020
ER -