@conference {8875501,
	title = {Enabling Container-Based Fog Computing with OpenStack},
	booktitle = {2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)},
	year = {2019},
	month = {July},
	pages = {1049-1056},
	abstract = {Operating system-level virtualization using containerization technologies have changed the world of application development and software by bringing flexibility, efficiency and new methods for managing and distributing software. Edge/Fog computing complements nowadays powerful centralized approach leveraging datacenters resources with a number of distributed fog nodes with relatively capable resources in order to provide advanced services in proximity to end users and data sources. In fact, this emerging paradigm provides ubiquitous processing abilities through scattered heterogeneous hardware with different energy availability and computational capabilities. This paper aims at presenting an extension of an IoT centric infrastructure Cloud framework, named Stack4Things, towards the edge through an integration with two of the OpenStack subsystems (i.e., Zun and Kuryr) that deal with containers management.},
	keywords = {Application development, Cloud, cloud computing, computer centres, container management, container-based fog computing, containerization technologies, containers, datacenter resources, distributed fog nodes, distributing software, Ecosystems, Edge/Fog computing, Internet of Things, IoT, IoT centric infrastructure cloud framework, Kuryr, Neutrons, OpenStack, OpenStack subsystems, operating system-level virtualization, Servers, software management, ubiquitous processing abilities, virtualisation, Virtualization, Zun},
	doi = {10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00181},
	author = {Zakaria Benomar and F. Longo and G. Merlino and A. Puliafito}
}
@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}
}
