@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}
}
@proceedings {Distefano201377,
	title = {Investigating mobile crowdsensing application performance},
	journal = {Proceedings of the 3rd ACM International Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications (DIVANet), Co-located with the 16th ACM Int. Conf. on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM)},
	year = {2013},
	note = {cited By 0; Conference of 3rd ACM Int. Symp. on Design and Analysis of Intelligent Vehicular Networks and Applications, DIVANet 2013, Held in Conjunction with the 16th ACM Int. Conf. on Modeling, Analysis and Simulation of Wireless and Mobile Systems, MSWiM 2013 ; Conference Date: 3 November 2013 Through 8 November 2013; Conference Code:101342},
	pages = {77-83},
	publisher = {ACM},
	address = {Barcelona, Spain, 3-8 November 2013},
	abstract = {<p>Mobile Crowdsensing (MCS) is an emerging distributed paradigm lying at the intersection between the Internet of Things and the volunteer/crowd-based approach. MCS applications are usually deployed on contributing nodes such as smart devices and mobiles, equipped by sensing resources that sample the physical environment and provide the sensed data, once filtered, aggregated and preprocessed, to the MCS application server. The MCS opportunistic approach unlocks new form of pervasive, participatory sensing applications, acquiring interests also in business contexts that call for adequate techniques and tools to drive architects and developers in MCS application design. Aim of this paper is to evaluate the performance of an MCS application though a stochastic model able to stochastically represent the overall MCS environment, thus providing a valid support to MCS application development. The Petri nets formalism is used due to its expressiveness and the capabilities to represent complex, dependent, non-Markovian, phenomena usually characterizing MCS environments. A specific MCS application is then evaluated to demonstrate the effectiveness of the proposed technique on a real case study. {\textcopyright} 2013 ACM.</p>
},
	keywords = {Application development, Application performance, Complex networks, crowdsensing, Design, Digital storage, Internet of Things (IOT), Participatory sensing applications, Performance, Petri nets, Physical environments, Stochastic models, Techniques and tools},
	isbn = {9781450323581},
	doi = {10.1145/2512921.2512931},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84889685098\&partnerID=40\&md5=15df22b992482173437dcd8080bbbc94},
	author = {Salvatore Distefano and Francesco Longo and Marco Scarpa}
}
