@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}
}
@article {Dautov201829822,
	title = {Data Processing in Cyber-Physical-Social Systems Through Edge Computing},
	journal = {IEEE Access - IEEE},
	volume = {6},
	year = {2018},
	note = {cited By 0},
	pages = {29822-29835},
	publisher = {Institute of Electrical and Electronics Engineers Inc.},
	abstract = {<p>Cloud and Fog computing have established a convenient and widely adopted approach for computation offloading, where raw data generated by edge devices in the Internet of Things (IoT) context is collected and processed remotely. This vertical offloading pattern, however, typically does not take into account increasingly pressing time constraints of the emerging IoT scenarios, in which numerous data sources, including human agents (i.e., Social IoT), continuously generate large amounts of data to be processed in a timely manner. Big data solutions could be applied in this respect, provided that networking issues and limitations related to connectivity of edge devices are properly addressed. Although edge devices are traditionally considered to be resource-constrained, main limitations refer to energy, networking, and memory capacities, whereas their ever-growing processing capabilities are already sufficient to be effectively involved in actual (big data) processing. In this context, the role of human agents is no longer limited to passive data generation, but can also include their voluntary involvement in relatively complex computations. This way, users can share their personal computational resources (i.e., mobile phones) to support collaborative data processing, thereby turning the existing IoT into a global cyber-physical-social system (CPSS). To this extent, this paper proposes a novel IoT/CPSS data processing pattern based on the stream processing technology, aiming to distribute the workload among a cluster of edge devices, involving mobile nodes shared by contributors on a voluntary basis, and paving the way for cluster computing at the edge. Experiments on an intelligent surveillance system deployed on an edge device cluster demonstrate the feasibility of the proposed approach, illustrating how its distributed in-memory data processing architecture can be effective. {\textcopyright} 2013 IEEE.</p>
},
	keywords = {Apache NiFi, Big Data, Cameras, Cellular telephone systems, cloud computing, Cluster computing, Computer architecture, Cyber physical social systems, Cyber Physical System, Edge computing, Fog computing, Horizontal and Vertical Offloading, Internet of Things, Media streaming, Network security, Servers, Stream processing, Streaming media},
	issn = {21693536},
	doi = {10.1109/ACCESS.2018.2839915},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047619039\&doi=10.1109\%2fACCESS.2018.2839915\&partnerID=40\&md5=48b52a73084c2f6396c4ce1dd6a690f4},
	author = {Rustem Dautov and Salvatore Distefano and Dario Bruneo and Francesco Longo and Giovanni Merlino and Antonio 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}
}
@proceedings {7092964,
	title = {How to Enhance Cloud Architectures to Enable Cross-Federation: Towards Interoperable Storage Providers},
	journal = { IEEE International Conference on Cloud Engineering (IC2E), Tempe, AZ, USA},
	year = {2015},
	month = {March},
	pages = {480-486},
	publisher = {IEEE Computer Society},
	address = {Washington D.C.},
	keywords = {Big Data, Big Data storage, CLEVER, CLEVER message oriented middleware, cloud architectures, cloud computing, cloud storage market, cross-federation, Ecosystems, federated cloud ecosystem, federation, Hadoop, Hadoop distribute file system, HDFS, inter-domain communication management, interoperable storage providers, Message systems, middleware, MOM, Protocols, public domain software, Resource management, Servers, small-medium cloud storage providers, Storage, storage management, XML, XMPP},
	doi = {10.1109/IC2E.2015.80},
	url = {http://conferences.computer.org/IC2E/2015/index.htm},
	author = {Maria Fazio and Antonio Celesti and Massimo Villari and Antonio Puliafito}
}
@conference {6726193,
	title = {Using Virtualization and Guacamole/VNC to Provide Adaptive User Interfaces to Disabled People in Cloud Computing},
	booktitle = {Ubiquitous Intelligence and Computing, 2013 IEEE 10th International Conference on and 10th International Conference on Autonomic and Trusted Computing (UIC/ATC)},
	year = {2013},
	month = {Dec},
	pages = {72-79},
	keywords = {adaptive user interfaces, Adaptive User Interfaces and Tools, assisted living, Assistive Technology, AT software, AT tools, Browsers, CLEVER, cloud computing, Computer architecture, Computers, disabled people, graphical user interfaces, Guacamole, handicapped aids, HTML5, HTML5 remote desktop gateway, HTML5-based Web interface, hypermedia markup languages, hypervisor, Internet, internetworking, Keyboards, middleware, networked computer, online front-ends, personalized virtual desktops, Psychological Factors, Remote desktop, SaaS, Servers, software as a service, VIM middleware, Virtual Box, virtual infrastructure manager middleware, virtual machine monitor, virtual machines, virtual network computing, virtualisation, Virtualization, VNC, Web browser, Web-accessed VM},
	doi = {10.1109/UIC-ATC.2013.42},
	author = {Mulfari, D. and Celesti, A. and Villari, M. and Puliafito, A.}
}
