@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 {Ghosh2014667,
	title = {Stochastic model driven capacity planning for an infrastructure-as-a-service cloud},
	journal = {IEEE Transactions on Services Computing - IEEE Computer Society},
	volume = {7},
	number = {4},
	year = {2014},
	note = {cited By 1},
	pages = {667-680},
	publisher = {Institute of Electrical and Electronics Engineers},
	abstract = {<p>From an enterprise perspective, one key motivation to transform the traditional IT management into Cloud is the cost reduction of the hosted services. In an Infrastructure-as-a-Service (IaaS) Cloud, virtual machine (VM) instances share the physical machines (PMs) in the provider{\textquoteright}s data center. With large number of PMs, providers can maintain low cost of service downtime at the expense of higher infrastructure and other operational costs (e.g., power consumption and cooling costs). Hence, determining the optimal PM capacity requirements that minimize the overall cost is of interest. In this paper, we show how a cost analysis and optimization framework can be developed using stochastic availability and performance models of an IaaS Cloud. Specifically, we study two cost minimization problems to address the capacity planning in an IaaS Cloud: 1) what is the optimal number of PMs that minimizes the total cost of ownership for a given downtime requirement set by service level agreements? and, 2) is it more economical to use cheaper but less reliable PMs or to use costlier but more reliable PMs for insuring the same availability characteristics? We use simulated annealing, a well-known stochastic search algorithm, to solve these optimization problems. Results from our analysis show that the optimal solutions are found within reasonable time. {\textcopyright} 2013 IEEE.</p>
},
	keywords = {Capacity planning, Capacity requirement, Clouds, Cost benefit analysis, Cost reduction, Downtime, Infrastructure as a service (IaaS), Maintenance, Optimization, Optimization framework, Optimization problems, Service Level Agreements, Simulated annealing, Stochastic models, Stochastic search algorithms, Stochastic systems, Total cost of ownership},
	issn = {19391374},
	doi = {10.1109/TSC.2013.44},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84919717849\&partnerID=40\&md5=34c70f14a9a3f838562335050a628985},
	author = {Rahul Ghosh and Francesco Longo and Ruofan Xia and Vijay K. Naik and Kishor S. Trivedi}
}
