Titlebook: Bringing Machine Learning to Software-Defined Networks; Zehua Guo Book 2022 The Author(s), under exclusive license to Springer Nature Sing

abnegate

Book 2022y taking advantage of existing machine learning techniques. The book elaborates on the formulation of each problem, explains design details for each scheme, and provides solutions by running mathematical optimization processes, conducting simulated experiments, and analyzing the experimental results.

legislate

Fermentation

DRILL

photopsia

Cecilia Pahlberg,Magnus Perssonamework 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.

期滿

使痛苦

JAMB

NAIVE

Multi-Agent Reinforcement Learning-Based Controller Load Balancing in SD-WANs,amework 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.

脫離

Amjad Hadjikhani,Pervez Ghauri,Jan JohansonIn this chapter, we introduce software-defined networking, and its two typical application scenarios: wide area networks and data center networks. We also briefly introduce emerging machine learning techniques to improve network performance that are used in the rest of this book.