@conference {8597889,
	title = {A Deep Reinforcement Learning Approach For Data Migration in Multi-Access Edge Computing},
	booktitle = {2018 ITU Kaleidoscope: Machine Learning for a 5G Future (ITU K)},
	year = {2018},
	month = {Nov},
	pages = {1-8},
	keywords = {5G, 5G mobile communication, cloud computing, computational resources, data migration, Deep Reinforcement Learning, deep reinforcement learning approach, distributed services, Edge computing, Keras machine learning, Keras machine learning framework, learning (artificial intelligence), Long Term Evolution, LTE, MEC scenarios, Multi-access Edge Computing, multiaccess edge computing, network performance, OMNeT++/SimuLTE simulator, parameter settings, push data, Quality of service, Reinforcement learning, Resource allocation, resource utilization, Servers, SimuLTE},
	doi = {10.23919/ITU-WT.2018.8597889},
	author = {F. D. Vita and D. Bruneo and A. Puliafito and G. Nardini and A. Virdis and G. Stea}
}
