76068 20191126134631.0 doi 10.1016/j.future.2017.12.062 sideral 104535 ART-2018-104535 eng Liu, B. Model-based sensitivity analysis of IaaS cloud availability 2018 Access copy available to the general public Unrestricted The increasing shift of various critical services towards Infrastructure-as-a-Service (IaaS) cloud data centers (CDCs) creates a need for analyzing CDCs’ availability, which is affected by various factors including repair policy and system parameters. This paper aims to apply analytical modeling and sensitivity analysis techniques to investigate the impact of these factors on the availability of a large-scale IaaS CDC, which (1) consists of active and two kinds of standby physical machines (PMs), (2) allows PM moving among active and two kinds of standby PM pools, and (3) allows active and two kinds of standby PMs to have different mean repair times. Two repair policies are considered: (P1) all pools share a repair station and (P2) each pool uses its own repair station. We develop monolithic availability models for each repair policy by using Stochastic Reward Nets and also develop the corresponding scalable two-level models in order to overcome the monolithic model''s limitations, caused by the large-scale feature of a CDC and the complicated interactions among CDC components. We also explore how to apply differential sensitivity analysis technique to conduct parametric sensitivity analysis in the case of interacting sub-models. Numerical results of monolithic models and simulation results are used to verify the approximate accuracy of interacting sub-models, which are further applied to examine the sensitivity of the large-scale CDC availability with respect to repair policy and system parameters. info:eu-repo/grantAgreement/ES/MINECO/TIN2014-58457-R This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 644869-DICE info:eu-repo/grantAgreement/EC/H2020/644869/EU/Developing Data-Intensive Cloud Applications with Iterative Quality Enhancements/DICE info:eu-repo/semantics/openAccess by-nc-nd http://creativecommons.org/licenses/by-nc-nd/3.0/es/ 5.768 2018 COMPUTER SCIENCE, THEORY & METHODS 8 / 104 = 0.077 2018 Q1 T1 0.835 2018 Computer Networks and Communications 2018 Q1 Software 2018 Q1 Hardware and Architecture 2018 Q1 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Chang, X. Han, Z. Trivedi, K. (orcid)0000-0001-7982-0359 Rodríguez, R.J. 83 (2018), 1-13 Future gener. comput. syst. Future Generation Computer Systems-The International Journal of Grid Computing Theory Methods and Applications 0167-739X 6228373 http://zaguan.unizar.es/record/76068/files/texto_completo.pdf Postprint 131335 http://zaguan.unizar.es/record/76068/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:76068 articulos driver 2019-11-26-13:41:09 ARTICLE