Abstract
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.
Original language | English |
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Title of host publication | SpringerBriefs in Computer Science |
Publisher | Springer |
Pages | 23-38 |
Number of pages | 16 |
DOIs | |
Publication status | Published - 2022 |
Publication series
Name | SpringerBriefs in Computer Science |
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ISSN (Print) | 2191-5768 |
ISSN (Electronic) | 2191-5776 |
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Guo, Z. (2022). Multi-Agent Reinforcement Learning-Based Controller Load Balancing in SD-WANs. In SpringerBriefs in Computer Science (pp. 23-38). (SpringerBriefs in Computer Science). Springer. https://doi.org/10.1007/978-981-19-4874-9_3