@article {Distefano2015629,
	title = {QoS Assessment of Mobile Crowdsensing Services},
	journal = {Journal of Grid Computing},
	volume = {13},
	number = {4},
	year = {2015},
	note = {cited By 2},
	pages = {629-650},
	publisher = {Springer Netherlands},
	abstract = {<p>The wide spreading of smart devices drives to develop distributed applications of increasing complexity, attracting efforts from both research and business communities. Recently, a new volunteer contribution paradigm based on participatory and opportunistic sensing is affirming in the Internet of Things scenario: Mobile Crowdsensing (MCS). A typical MCS application considers smart devices as contributing sensors able to produce geolocalized data about the physical environment, then collected by a remote application server for processing. The growing interest on MCS allows to think about its possible exploitation in commercial context. This calls for adequate methods able to support MCS service providers in design choices, implementing mechanisms for the quality of service (QoS) assessment while dealing with complex time-dependent phenomena and churning issues due to contributors that unpredictably join and leave the MCS system. In this paper, we propose an analytical modeling framework based on stochastic Petri nets to evaluate QoS metrics of a class of MCS services. This method requires to extend the Petri net formalism by specifying a marking dependency semantics for non-exponentially distributed transitions. The approach is then applied to an MCS application example deriving some QoS measures that can drive quantitative evaluation and characterization of the {\textquotedblleft}crowd{\textquotedblright} behavior. {\textcopyright} 2015, Springer Science+Business Media Dordrecht.</p>
},
	keywords = {crowdsensing, Digital storage, Distributed applications, Marking dependency, Non-Markovian, Performability, Petri nets, Quality of service, Quality of service (QoS) assessments, Quantitative evaluation, Semantics, Stochastic systems, Time dependent phenomena},
	issn = {15707873},
	doi = {10.1007/s10723-015-9338-7},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958155341\&doi=10.1007\%2fs10723-015-9338-7\&partnerID=40\&md5=3bd7e36a37ab06a3acabb37a21b90ab1},
	author = {Salvatore Distefano and Francesco Longo and Marco Scarpa}
}
@proceedings {Distefano2014255,
	title = {Non-Markovian modeling of a BladeCenter chassis midplane},
	journal = {Proceedings of the 11th European Workshop on Computer Performance Engineering (EPEW)},
	volume = {8721 Lecture Notes in Computer Science},
	year = {2014},
	note = {cited By 0; Conference of 11th European Workshop on Computer Performance Engineering, EPEW 2014 ; Conference Date: 11 September 2014 Through 12 September 2014; Conference Code:107431},
	pages = {255-269},
	publisher = {Springer Verlag},
	address = {Florence, Italy, 11-12 September 2014},
	abstract = {<p>In distributed contexts such as Cloud computing, the reliability and availability of the provided resources and services have to be assured in order to meet user requirements. At the infrastructure level, this specification is translated into tighter ones on the datacenter hosting physical resources. In this paper, starting from a real case study of the IBM BladeCenter, we provide a technique for the quantitative evaluation of datacenter infrastructure availability. The proposed technique allows one to take into account both aging phenomena and multiple operating conditions. In particular, one subsystem of the BladeCenter, the chassis midplane, is studied. Indeed, based on the stochastic characterization of the midplane reliability through statistic measurements, a model dealing with the non-exponential failure time distribution thus obtained is evaluated to demonstrate the suitability and the effectiveness of the proposed technique. {\textcopyright} 2014 Springer International Publishing.</p>
},
	keywords = {Aging phenomena, Chassis, Failure-time distribution, Non-Markovian modeling, Operating condition, Physical resources, Quantitative evaluation, Reliability and availability, Stochastic models, Stochastic systems, User requirements},
	isbn = {9783319108841},
	issn = {03029743},
	doi = {10.1007/978-3-319-10885-8_18},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84906969232\&partnerID=40\&md5=6840d4a28b6ed80a602f4db06c10343b},
	author = {Salvatore Distefano and Francesco Longo and Marco Scarpa and Kishor S. Trivedi}
}
