Abstract
5G Networks and Multi-access Edge Computing (MEC) will serve various use cases of emerging technologies with a wide range of requirements of multiple resources. Network slicing being the promising technology of providing virtual infrastructure, which can be customized according to the different use case requirements. Network slicing at the edge becomes new paradigm to providing cloud capabilities at the edge of Radio Access Network (RAN). In this chapter, we propose a Reinforcement Learning-based Deep Q-Network (DQN) approach to learn an optimal policy by constantly interacting with the task off-loader (environment) where the problem is modelled as a Markov decision process (MDP) while using a deep neural network to estimate the reward and transition functions. The simulation results show that the learned policy based on the proposed schema provides optimal compute performance while maintaining lower networking costs.
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