@article {Ghosh201457,
	title = {Scalable analytics for IaaS cloud availability},
	journal = {IEEE Transactions on Cloud Computing - IEEE Computer Society},
	volume = {2},
	number = {1},
	year = {2014},
	note = {cited By 5},
	pages = {57-70},
	publisher = {Institute of Electrical and Electronics Engineers Inc.},
	abstract = {<p>In a large Infrastructure-as-a-Service (IaaS) cloud, component failures are quite common. Such failures may lead to occasional system downtime and eventual violation of Service Level Agreements (SLAs) on the cloud service availability. The availability analysis of the underlying infrastructure is useful to the service provider to design a system capable of providing a defined SLA, as well as to evaluate the capabilities of an existing one. This paper presents a scalable, stochastic model-driven approach to quantify the availability of a large-scale IaaS cloud, where failures are typically dealt with through migration of physical machines among three pools: hot (running), warm (turned on, but not ready), and cold (turned off). Since monolithic models do not scale for large systems, we use an interacting Markov chain based approach to demonstrate the reduction in the complexity of analysis and the solution time. The three pools are modeled by interacting sub-models. Dependencies among them are resolved using fixed-point iteration, for which existence of a solution is proved. The analytic-numeric solutions obtained from the proposed approach and from the monolithic model are compared. We show that the errors introduced by interacting sub-models are insignificant and that our approach can handle very large size IaaS clouds. The simulative solution is also considered for the proposed model, and solution time of the methods are compared. {\textcopyright} 2014 IEEE.</p>
},
	keywords = {Availability, availability analysis, cloud computing, Downtime, Existence of a solutions, Infrastructure as a service (IaaS), Iterative methods, Lakes, Maintenance, Markov processes, Model driven approach, Numeric solutions, Service level agreement (SLAs), simulation, Stochastic models, Stochastic reward nets, Stochastic systems},
	issn = {21687161},
	doi = {10.1109/TCC.2014.2310737},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84906976356\&partnerID=40\&md5=a63dc67c92ef7620c8a3b33aca08348b},
	author = {Rahul Ghosh and Francesco Longo and Flavio Frattini and Stefano Russo and Kishor S. Trivedi}
}
@article {Bruneo20132090,
	title = {Stochastic evaluation of QoS in service-based systems},
	journal = {IEEE Transactions on Parallel and Distributed Systems - IEEE Computer Society},
	volume = {24},
	number = {10},
	year = {2013},
	note = {cited By 7},
	pages = {2090-2099},
	abstract = {<p>WS-BPEL language has become the industrial standard to design and orchestrate modular applications, formalizing service compositions and business relationships among providers and consumers. Once service level agreements (SLAs) among the parties are established, effective tools for evaluating appropriate measurements have to be developed to meet the requirements. However, the design of quality of service (QoS)-guaranteed composed Web services (WSes) still requires several efforts. This work aims at proposing a complete method to study the QoS of a composed WS at design time, i.e., when the process is specified by using WS-BPEL. Starting from the nonfunctional properties of the WS to compose, we propose a technique to derive non-Markovian stochastic Petri net (NMSPN) models from WS-BPEL processes, with the final goal of evaluating parameters such as the service time distribution and the service reliability. To demonstrate the effectiveness of the proposed method and to validate the obtained model, a nontrivial example implementing a travel agency flight reservation process, exposed as a synchronous composed WS, is investigated. {\textcopyright} 1990-2012 IEEE.</p>
},
	keywords = {Business Process, Business relationships, Design, Information services, Non functional properties, Performance, Quality of service, Random access storage, Reliability, Service level agreement (SLAs), Service oriented architecture (SOA), Service time distribution, Stochastic Petri Nets, Web services, WS-BPEL},
	issn = {10459219},
	doi = {10.1109/TPDS.2012.313},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84883410035\&partnerID=40\&md5=cdebb3fe5a36e131fd3a9485d01fe3c6},
	author = {Dario Bruneo and Salvatore Distefano and Francesco Longo and Marco Scarpa}
}
