000070841 001__ 70841
000070841 005__ 20190709135516.0
000070841 0247_ $$2doi$$a10.1093/comjnl/bxx049
000070841 0248_ $$2sideral$$a99881
000070841 037__ $$aART-2017-99881
000070841 041__ $$aeng
000070841 100__ $$aChang, Xiaolin
000070841 245__ $$aModeling and analysis of high availability techniques in a virtualized system
000070841 260__ $$c2017
000070841 5060_ $$aAccess copy available to the general public$$fUnrestricted
000070841 5203_ $$aAvailability 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.
000070841 536__ $$9info:eu-repo/grantAgreement/ES/UZ/CUD2016-TEC-06$$9info:eu-repo/grantAgreement/ES/MICINN/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
000070841 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000070841 590__ $$a0.792$$b2017
000070841 591__ $$aCOMPUTER SCIENCE, THEORY & METHODS$$b75 / 103 = 0.728$$c2017$$dQ3$$eT3
000070841 591__ $$aCOMPUTER SCIENCE, SOFTWARE ENGINEERING$$b85 / 104 = 0.817$$c2017$$dQ4$$eT3
000070841 591__ $$aCOMPUTER SCIENCE, HARDWARE & ARCHITECTURE$$b47 / 52 = 0.904$$c2017$$dQ4$$eT3
000070841 591__ $$aCOMPUTER SCIENCE, INFORMATION SYSTEMS$$b131 / 148 = 0.885$$c2017$$dQ4$$eT3
000070841 592__ $$a0.319$$b2017
000070841 593__ $$aComputer Science (miscellaneous)$$c2017$$dQ2
000070841 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000070841 700__ $$aWang, Tianju
000070841 700__ $$0(orcid)0000-0001-7982-0359$$aRodríguez, Ricardo J.$$uUniversidad de Zaragoza
000070841 700__ $$aZhang, Zhenjiang
000070841 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000070841 773__ $$g61, 2 (2017), 180-198$$pComput. j.$$tCOMPUTER JOURNAL$$x0010-4620
000070841 8564_ $$s245647$$uhttps://zaguan.unizar.es/record/70841/files/texto_completo.pdf$$yPostprint
000070841 8564_ $$s66464$$uhttps://zaguan.unizar.es/record/70841/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000070841 909CO $$ooai:zaguan.unizar.es:70841$$particulos$$pdriver
000070841 951__ $$a2019-07-09-11:55:23
000070841 980__ $$aARTICLE