A Deep Reinforcement Learning based Network Management System in Smart Identifier Network

As a new large-scale deployment network, Smart Identifier Network (SINET) points out the identity/location binding, which is one of the root causes of current Internet's problem. In order to ensure the controllable manageability and large-scale deployment of the SINET, a network management system is of great essence. And considering the single point of failure of centralized network management, this paper proposes a Deep Reinforcement Learning (DRL) based domain network management system to manage the devices and achieve the reasonable allocation of management resources, where we consider the problem as a Markov Decision Process (MDP), including how to settle the new device and the change of the number of devices in each management domain at each time. By quantifying the cost function, we want to minimize it in a period of time, that is, to maximize the long-term expected reward value. The experiments show that our agent can automatically learn the environment, and the results will gradually reach convergence after certain iterations.

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