TY - CHAP
T1 - Multi-Agent Reinforcement Learning-Based Controller Load Balancing in SD-WANs
AU - Guo, Zehua
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - In this chapter, we introduce a dynamic controller workload balancing scheme named MARVEL using emerging MARL for switch migration. We design a DRL framework for each agent in the MARL model. The DRL-based solution takes the workload pattern in the control plane as input and generates the migration decision as the output. When training is done, the DRL agent can quickly and accurately decide how to migrate switches among the controllers.
AB - In this chapter, we introduce a dynamic controller workload balancing scheme named MARVEL using emerging MARL for switch migration. We design a DRL framework for each agent in the MARL model. The DRL-based solution takes the workload pattern in the control plane as input and generates the migration decision as the output. When training is done, the DRL agent can quickly and accurately decide how to migrate switches among the controllers.
UR - http://www.scopus.com/inward/record.url?scp=85139861097&partnerID=8YFLogxK
U2 - 10.1007/978-981-19-4874-9_3
DO - 10.1007/978-981-19-4874-9_3
M3 - Chapter
AN - SCOPUS:85139861097
T3 - SpringerBriefs in Computer Science
SP - 23
EP - 38
BT - SpringerBriefs in Computer Science
PB - Springer
ER -