000076068 001__ 76068 000076068 005__ 20191126134631.0 000076068 0247_ $$2doi$$a10.1016/j.future.2017.12.062 000076068 0248_ $$2sideral$$a104535 000076068 037__ $$aART-2018-104535 000076068 041__ $$aeng 000076068 100__ $$aLiu, B. 000076068 245__ $$aModel-based sensitivity analysis of IaaS cloud availability 000076068 260__ $$c2018 000076068 5060_ $$aAccess copy available to the general public$$fUnrestricted 000076068 5203_ $$aThe 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. 000076068 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/TIN2014-58457-R$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 644869-DICE$$9info:eu-repo/grantAgreement/EC/H2020/644869/EU/Developing Data-Intensive Cloud Applications with Iterative Quality Enhancements/DICE 000076068 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/ 000076068 590__ $$a5.768$$b2018 000076068 591__ $$aCOMPUTER SCIENCE, THEORY & METHODS$$b8 / 104 = 0.077$$c2018$$dQ1$$eT1 000076068 592__ $$a0.835$$b2018 000076068 593__ $$aComputer Networks and Communications$$c2018$$dQ1 000076068 593__ $$aSoftware$$c2018$$dQ1 000076068 593__ $$aHardware and Architecture$$c2018$$dQ1 000076068 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion 000076068 700__ $$aChang, X. 000076068 700__ $$aHan, Z. 000076068 700__ $$aTrivedi, K. 000076068 700__ $$0(orcid)0000-0001-7982-0359$$aRodríguez, R.J. 000076068 773__ $$g83 (2018), 1-13$$pFuture gener. comput. syst.$$tFuture Generation Computer Systems-The International Journal of Grid Computing Theory Methods and Applications$$x0167-739X 000076068 8564_ $$s6228373$$uhttps://zaguan.unizar.es/record/76068/files/texto_completo.pdf$$yPostprint 000076068 8564_ $$s131335$$uhttps://zaguan.unizar.es/record/76068/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint 000076068 909CO $$ooai:zaguan.unizar.es:76068$$particulos$$pdriver 000076068 951__ $$a2019-11-26-13:41:09 000076068 980__ $$aARTICLE