TY - GEN
T1 - Game theoretic Conflict Resolution Mechanism for Cognitive Autonomous Networks
AU - Banerjee, Anubhab
AU - Mwanje, Stephen S.
AU - Carle, Georg
N1 - Publisher Copyright:
© 2020 SCS.
PY - 2020/7
Y1 - 2020/7
N2 - Cognitive Autonomous Networks (CAN) advance network automation by using Cognitive Functions (CFs) which learn optimal behavior through interaction with the network. However, as in self Organizing Networks (SON), CFs encounter conflicts due to overlap in parameters or objectives. Owing to the nondeterministic behavior of CFs, their conflicts cannot be resolved using SON-style rule-based approaches. This paper proposes the Cognitive Bargaining Mechanism (CBM) as the optimal generic way for resolving- A ny type of conflict among CFs, conflict among any number of CFs and any number of simultaneously existing conflicts among CFs. With the CAN modeled as a multi-agent system (MAS), CBM uses Nash's Social Welfare Function (NSWF) to compute a compromise among CFs that is fair and optimal for the collective interest of the system. To prove the feasibility of the approach, we model three different CAN scenarios in Python and show the resulting configurations when a CBM-enabled controller is used to resolve all the possible conflicts in the CAN.
AB - Cognitive Autonomous Networks (CAN) advance network automation by using Cognitive Functions (CFs) which learn optimal behavior through interaction with the network. However, as in self Organizing Networks (SON), CFs encounter conflicts due to overlap in parameters or objectives. Owing to the nondeterministic behavior of CFs, their conflicts cannot be resolved using SON-style rule-based approaches. This paper proposes the Cognitive Bargaining Mechanism (CBM) as the optimal generic way for resolving- A ny type of conflict among CFs, conflict among any number of CFs and any number of simultaneously existing conflicts among CFs. With the CAN modeled as a multi-agent system (MAS), CBM uses Nash's Social Welfare Function (NSWF) to compute a compromise among CFs that is fair and optimal for the collective interest of the system. To prove the feasibility of the approach, we model three different CAN scenarios in Python and show the resulting configurations when a CBM-enabled controller is used to resolve all the possible conflicts in the CAN.
KW - Cognitive Autonomous Networks
KW - Conflict Resolution
KW - Game Theory
KW - Machine Learning
KW - Nash's Social Welfare Function
UR - http://www.scopus.com/inward/record.url?scp=85093851558&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85093851558
T3 - 2020 International Symposium on Performance Evaluation of Computer and Telecommunication Systems, SPECTS 2020 - Proceedings
BT - 2020 International Symposium on Performance Evaluation of Computer and Telecommunication Systems, SPECTS 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 International Symposium on Performance Evaluation of Computer and Telecommunication Systems, SPECTS 2020
Y2 - 20 July 2020 through 22 July 2020
ER -