5G technology promises to improve the network performance by allowing users to seamlessly access distributed services in a powerful way. In this perspective, Multi-access Edge Computing (MEC) is a relevant paradigm that push data and computational resources nearby users with the final goal to reduce latencies and improve resource utilization. Such a scenario requires strong policies in order to react to the dynamics of the environment also taking into account multiple parameter settings. In this paper, we propose a deep reinforcement learning approach that is able to manage data migration in MEC scenarios by learning during the system evolution. We set up a simulation environment based on the OMNeT++/SimuLTE simulator integrated with the Keras machine learning framework. Preliminary results showing the feasibility of the proposed approach are discussed.

A Deep Reinforcement Learning Approach for Data Migration in Multi-access Edge Computing

Giovanni Nardini;Antonio Virdis;Giovanni Stea
2018

Abstract

5G technology promises to improve the network performance by allowing users to seamlessly access distributed services in a powerful way. In this perspective, Multi-access Edge Computing (MEC) is a relevant paradigm that push data and computational resources nearby users with the final goal to reduce latencies and improve resource utilization. Such a scenario requires strong policies in order to react to the dynamics of the environment also taking into account multiple parameter settings. In this paper, we propose a deep reinforcement learning approach that is able to manage data migration in MEC scenarios by learning during the system evolution. We set up a simulation environment based on the OMNeT++/SimuLTE simulator integrated with the Keras machine learning framework. Preliminary results showing the feasibility of the proposed approach are discussed.
978-92-61-26921-0
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11568/932216
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