Title
Evaluating the Performability of Systems with Background Jobs
Abstract
As most computer systems are expected to remain operational 24 hours a day, 7 days a week, they must complete maintenance work while in operation. This work is in addition to the regular tasks of the system and its purpose is to improve system reliability and availability. Nonetheless, additional work in the system, although labeled as best effort or low priority, still affects the performance of foreground tasks, especially if background/foregroundwork is non-preemptive. In this paper, we propose an analytic model to evaluate the performance trade-offs of the amount of background work that a storage system can sustain. The proposed model results in a quasi-birth-death (QBD) process that is analytically tractable. Detailed experimentation using a variety of workloads shows that under dependent arrivals both foreground and background performance strongly depends on system load. In contrast, if arrivals of foreground jobs are independent, performance sensitivity to load is reduced. The model identifies dependence in the arrivals of foreground jobs as an important characteristic that controls the decision of how much background load the system can accept to maintain high availability and performance gains.
Year
DOI
Venue
2006
10.1109/DSN.2006.33
DSN
Keywords
Field
DocType
system load,idle periods,storage system,storage systems,system reliability,markov modulated poisson process,qbd.,computer system,foreground/backgroundjobs,background performance,performance trade-offs,performance sensitivity,additional work,background jobs,performance gain,foreground job,qbd,resource allocation,best effort,high availability,computer science,markov processes,availability,reliability
Markov process,Computer data storage,Computer science,Real-time computing,Resource allocation,Digital storage,High availability,Analytic model,Reliability engineering,Distributed computing
Conference
ISBN
Citations 
PageRank 
0-7695-2607-1
2
0.38
References 
Authors
13
5
Name
Order
Citations
PageRank
Qi Zhang141422.77
Ningfang Mi266447.66
Evgenia Smirni31857161.97
Alma Riska468348.63
Erik Riedel51037142.99