TY - GEN
T1 - Multi-Agent Graph Convolutional Reinforcement Learning for Intelligent Load Balancing
AU - Houidi, Omar
AU - Bakri, Sihem
AU - Zeghlache, Djamal
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022/1/1
Y1 - 2022/1/1
N2 - A smart Load Balancing (LB) policy based on Graph Convolutional Multi-Agent Reinforcement Learning (GC-MARL) is proposed to improve load balancing in networks beyond what can be realized by traditional methods and state of the art machine learning based approaches. GC-MARL models the network as a graph and derives through a graph convolutional method the policy that splits traffic flows across end-to-end candidate paths while meeting application QoE requirements. The proposed method uses the throughput and the delay, observed at the network level, as the key performance indicators embedded in the reward expression as opposed to observing QoE at the application level. The results confirm the effectiveness of the proposed solution in terms of KPIs (such as throughput, delay, jitter, packet loss), and KQIs (such as QoE, average video bitrate, stalling, etc...).
AB - A smart Load Balancing (LB) policy based on Graph Convolutional Multi-Agent Reinforcement Learning (GC-MARL) is proposed to improve load balancing in networks beyond what can be realized by traditional methods and state of the art machine learning based approaches. GC-MARL models the network as a graph and derives through a graph convolutional method the policy that splits traffic flows across end-to-end candidate paths while meeting application QoE requirements. The proposed method uses the throughput and the delay, observed at the network level, as the key performance indicators embedded in the reward expression as opposed to observing QoE at the application level. The results confirm the effectiveness of the proposed solution in terms of KPIs (such as throughput, delay, jitter, packet loss), and KQIs (such as QoE, average video bitrate, stalling, etc...).
KW - Graph Convolutional Network
KW - Multi-Agent
KW - QoE Optimization
KW - Reinforcement Learning
KW - Smart Load Balancing
UR - https://www.scopus.com/pages/publications/85133167200
U2 - 10.1109/NOMS54207.2022.9789872
DO - 10.1109/NOMS54207.2022.9789872
M3 - Conference contribution
AN - SCOPUS:85133167200
T3 - Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022: Network and Service Management in the Era of Cloudification, Softwarization and Artificial Intelligence, NOMS 2022
BT - Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022
A2 - Varga, Pal
A2 - Granville, Lisandro Zambenedetti
A2 - Galis, Alex
A2 - Godor, Istvan
A2 - Limam, Noura
A2 - Chemouil, Prosper
A2 - Francois, Jerome
A2 - Pahl, Marc-Oliver
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE/IFIP Network Operations and Management Symposium, NOMS 2022
Y2 - 25 April 2022 through 29 April 2022
ER -