@proceedings {Bruneo2015797,
	title = {Analytical Modeling of Reactive Autonomic Management Techniques in IaaS Clouds},
	journal = {Proceedings - 2015 IEEE 8th International Conference on Cloud Computing, CLOUD 2015},
	year = {2015},
	note = {cited By 2; Conference of 8th IEEE International Conference on Cloud Computing, CLOUD 2015 ; Conference Date: 27 June 2015 Through 2 July 2015; Conference Code:116940},
	pages = {797-804},
	publisher = {Institute of Electrical and Electronics Engineers Inc.},
	address = {New York (USA)},
	abstract = {<p>Cloud computing infrastructures provide services to a wide number of users whose behavior can deeply change at the occurrence of particular events. To correctly handle such situations a cloud infrastructure have to be reconfigured in a way that does not cause degradation in the overall performance. Otherwise, the quality of service specified in the service level agreement could be violated. To prevent such situations, the infrastructure could be organized as an autonomic system where self-adaptation and self-configuration techniques are implemented. Appropriate design choices become important in order not to fail in this goal. We propose a technique, based on a Petri net model and a specific analytical analysis approach, to represent Infrastructure-as-a-Service (IaaS) systems in the case in which the load conditions can suddenly change and reactive autonomic management techniques are applied to mitigate the consequences of the change. The model we propose is able to appropriately evaluate performance metrics in such critical situations making it suitable as a design tool for IaaS cloud systems. {\textcopyright} 2015 IEEE.</p>
},
	keywords = {Autonomic management, cloud computing, Cloud computing infrastructures, Cloud infrastructures, Iaas clouds, Industrial management, Infrastructure as a service (IaaS), Performance metrics, Petri nets, Quality of service, Resiliency, Service Level Agreements, Stochastic reward nets, Stochastic systems},
	isbn = {9781467372879},
	doi = {10.1109/CLOUD.2015.110},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84960155425\&doi=10.1109\%2fCLOUD.2015.110\&partnerID=40\&md5=2aa09923889aea0bc667deb1d6d250d8},
	author = {Dario Bruneo and Francesco Longo and Rahul Ghosh and Marco Scarpa and Antonio Puliafito and Kishor Trivedi}
}
@article {Bruneo20111213,
	title = {Performance analysis of job dissemination techniques in Grid systems},
	journal = {Concurrency Computation Practice and Experience - John Wiley \& Sons, Inc.},
	volume = {23},
	number = {11},
	year = {2011},
	note = {cited By 2},
	pages = {1213-1235},
	abstract = {<p>In the last few years, remarkable efforts have been made to extend the Grid paradigm to commercial solutions. Business-oriented grids call for effective Quality of Service strategies able to adapt to different user requirements and to address Service Level Agreements. Performance analysis and prediction with respect to different load conditions or management policies are required to define such strategies. However, the highly distributed nature of Grid systems and the presence of distinct administrative domains make it difficult to carry out performance estimations. In fact, several parameters are involved and the autonomy of each site could make it complex to set them in a proper way. In this paper, we present a non-Markovian Stochastic Petri Net methodology that allows to conduct performance analysis of Grid systems focusing on aspects related to the Virtual Organization as a whole. In particular, different job allocation techniques can be evaluated with respect to both user and provider points-of-view. The influence of different information update policies on the accuracy of the allocation schemes can also be investigated, highlighting the costs/benefits in terms of job waiting time, service availability, and system utilization. The proposed methodology is designed to be as general as possible and it can be applied to analyze a gLite Grid infrastructure taken as case study. {\textcopyright} 2011 John Wiley \&amp; Sons, Ltd.</p>
},
	keywords = {Grid computing, Grid infrastructures, Grid systems, Information updates, Job allocation, Load condition, Management policy, Non-Markovian, Performance analysis, Performance estimation, performance measurements, Petri nets, Quality of service, Random access storage, Service availability, Service Level Agreements, Stochastic Petri Nets, Stochastic systems, System utilization, User requirements, Virtual organization, Waiting-time},
	issn = {15320626},
	doi = {10.1002/cpe.1697},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79960133545\&partnerID=40\&md5=a557a0ec6dd02249f31a6a0960e2bc92},
	author = {Dario Bruneo and Francesco Longo and Marco Scarpa and Antonio Puliafito}
}
