70841 20190709135516.0 doi 10.1093/comjnl/bxx049 sideral 99881 ART-2017-99881 eng Chang, Xiaolin Modeling and analysis of high availability techniques in a virtualized system 2017 Access copy available to the general public Unrestricted Availability evaluation of a virtualized system is critical to the wide deployment of cloud computing services. Time-based, prediction-based rejuvenation of virtual machines (VM) and virtual machine monitors, VM failover and live VM migration are common high-availability (HA) techniques in a virtualized system. This paper investigates the effect of combination of these availability techniques on VM availability in a virtualized system where various software and hardware failures may occur. For each combination, we construct analytic models rejuvenation mechanisms to improve VM availability; (2) prediction-based rejuvenation enhances VM availability much more than time-based VM rejuvenation when prediction successful probability is above 70%, regardless failover and/or live VM migration is also deployed; (3) failover mechanism outperforms live VM migration, although they can work together for higher availability of VM. In addition, they can combine with software rejuvenation mechanisms for even higher availability; (4) and time interval setting is critical to a time-based rejuvenation mechanism. These analytic results provide guidelines for deploying and parameter setting of HA techniques in a virtualized system. info:eu-repo/grantAgreement/ES/UZ/CUD2016-TEC-06 info:eu-repo/grantAgreement/ES/MICINN/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 All rights reserved http://www.europeana.eu/rights/rr-f/ 0.792 2017 COMPUTER SCIENCE, THEORY & METHODS 75 / 103 = 0.728 2017 Q3 T3 COMPUTER SCIENCE, SOFTWARE ENGINEERING 85 / 104 = 0.817 2017 Q4 T3 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE 47 / 52 = 0.904 2017 Q4 T3 COMPUTER SCIENCE, INFORMATION SYSTEMS 131 / 148 = 0.885 2017 Q4 T3 0.319 2017 Computer Science (miscellaneous) 2017 Q2 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Wang, Tianju (orcid)0000-0001-7982-0359 Rodríguez, Ricardo J. Universidad de Zaragoza Zhang, Zhenjiang 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. 61, 2 (2017), 180-198 Comput. j. COMPUTER JOURNAL 0010-4620 245647 http://zaguan.unizar.es/record/70841/files/texto_completo.pdf Postprint 66464 http://zaguan.unizar.es/record/70841/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:70841 articulos driver 2019-07-09-11:55:23 ARTICLE