@article {Bruneo20153052,
	title = {Modeling and Evaluation of Energy Policies in Green Clouds},
	journal = {IEEE Transactions on Parallel and Distributed Systems},
	volume = {26},
	number = {11},
	year = {2015},
	note = {cited By 0},
	pages = {3052-3065},
	publisher = {IEEE Computer Society},
	abstract = {<p>Following the as-a-service philosophy, a cloud service provider offers computing utilities in the form of virtual resources instantiated on top of a physical infrastructure. In order to meet business requirements still providing high-quality services, performance evaluation needs to be carefully carried out with the aim of optimizing data center utilization and increasing user satisfaction. In this context, power efficiency plays a critical role pushing service providers towards the application of innovative green strategies. In this paper, we present an analytical framework, based on stochastic reward nets, that allows to evaluate different resource allocation policies in a green cloud. A use case is shown in order to illustrate the approach, modeling scattering and saturation allocation policies and comparing them to a purely physical data center scenario. A validation of the proposed model against the CloudSim framework is presented and several numerical results are provided, demonstrating the effectiveness of the approach as a powerful tool for a cloud service provider to perform well-informed decisions about theresource allocation policies to be enforced. {\textcopyright} 1990-2012 IEEE.</p>
},
	keywords = {Allocation policies, Business requirement, cloud computing, Cloud service providers, Distributed database systems, Green Computing, High quality service, Performance evaluation, Quality control, Resource allocation, Resource allocation policy, Stochastic reward nets, Stochastic systems},
	issn = {10459219},
	doi = {10.1109/TPDS.2014.2364194},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84944075521\&partnerID=40\&md5=ee939ccd602bffca9ab90abdfd8285d9},
	author = {Dario Bruneo and Audric Lhoas and Francesco Longo and Antonio Puliafito}
}
@proceedings {Bruneo201384,
	title = {Analytical evaluation of resource allocation policies in green IaaS clouds},
	journal = {Proceedings of the IEEE 3rd International Conference on Cloud and Green Computing (CGC) Co-located with the IEEE 3rd International Conference on Social Computing and Its Applications (SCA)},
	year = {2013},
	note = {cited By 1; Conference of 3rd IEEE International Conference on Cloud and Green Computing, CGC 2013, Held Jointly with the 3rd IEEE International Conference on Social Computing and Its Applications, SCA 2013 ; Conference Date: 30 September 2013 Through 2 October 2013; Conference Code:102391},
	pages = {84-91},
	publisher = {IEEE Computer Society},
	address = {Karlsruhe, Germany, 30 September - 2 October 2013},
	abstract = {<p>Cloud systems represent the new ICT frontier where computing utilities are offered in terms of virtual instances, following the so called as-a-service philosophy. Different commercial solutions have been already put in place but several aspects need to be faced in order to provide high-value services able to meet business requirements. In particular, performance evaluation plays a critical role being strictly related to data center optimization and user satisfaction. Moreover, the use of large data centers able to respond to the high service demand has increased the attention to power efficiency, thus calling for green solutions and energy-aware strategies that jointly consider environmental and economical aspects. In order to design powerful strategies able to meet the quality of service requirements still reducing the energy costs, performance analysis frameworks are needed. In this paper, we present an analytical model, based on stochastic reward nets, that is able to easily implement resource allocation strategies in a green infrastructure as-a-service cloud. The model is organized into layers that represent the virtual resource pool and the physical machines. Different allocation algorithms (i.e., scattering, saturation) have been implemented by properly managing the coordination between the model layers. Numerical results are provided that demonstrate the effectiveness of the proposed approach. {\textcopyright} 2013 IEEE.</p>
},
	keywords = {Analytical evaluation, cloud computing, Distributed computer systems, Green Computing, Performance analysis, Performance evaluation, Quality of service, Resource allocation, Resource allocation policy, Resource allocation strategies, Service requirements, Stochastic reward nets, Stochastic systems},
	isbn = {9780769551142},
	doi = {10.1109/CGC.2013.21},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84893329592\&partnerID=40\&md5=369f37efc4aabea38300336d819db864},
	author = {Dario Bruneo and Audric Lhoas and Francesco Longo and Antonio Puliafito}
}
