| Summary: | One of the models in the literature for modeling the behavior of the brain is the Bayesian attractor model, which is a kind of machine-learning algorithm. According to this model, the brain assigns stochastic variables to possible decisions (attractors) and chooses one of them when enough evidence is collected from sensory systems to achieve a confidence level high enough to make a decision. In this paper, we introduce a software defined networking (SDN) application based on a brain-inspired Bayesian attractor model for identification of the current traffic pattern for the supervision and automation of Internet of things (IoT) networks that exhibit a limited number of traffic patterns. In a real SDN testbed, we demonstrate that our SDN application can identify the traffic patterns using a limited set of fluctuating network statistics of edge link utilization. Moreover, we show that our application can improve core link utilization and the power efficiency of IoT networks by immediately applying a pre-calculated network configuration optimized by traffic engineering with network slicing for the identified pattern.
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