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Author Machida, Fumio ♦ Nicola, Victor F ♦ Trivedi, Kishor S.
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2014
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Job completion time ♦ Rejuvenation ♦ Semi-Markov process ♦ Server virtualization ♦ Software aging
Abstract This article analyzes the completion time of a job running on a virtualized server subject to software aging and rejuvenation in a virtual machine monitor (VMM). A job running on the server may be interrupted by virtual machine (VM) failure, VMM failure or VMM rejuvenation. The job interruption is categorized as either preemptive-repeat $(\textit{prt}),$ in which case the interrupted job needs to restart from the beginning, or preemptive-resume $(\textit{prs}),$ in which case the job resumes execution from the point of interruption. Using a semi-Markov process (SMP) to model the server behavior, the steady-state server availability is computed and the theory developed in Kulkarni et al. [1987] is used to obtain the Laplace-Stieltjes transform (LST) of the job completion time. In the numerical experiments, we introduce four types of aging behavior of VMM. The effectiveness of VMM rejuvenation on job completion time is discussed in association with the type of interruption it causes and the VMM aging type. With our parameter settings, VMM rejuvenation with $\textit{prs}$ job interruption improves the performance of job execution regardless of the aging type, with performance degradation is taken into account.
ISSN 15504832
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2014-01-01
Publisher Place New York
e-ISSN 15504840
Journal ACM Journal on Emerging Technologies in Computing Systems (JETC)
Volume Number 10
Issue Number 1
Page Count 26
Starting Page 1
Ending Page 26

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Source: ACM Digital Library