NDLI: Estimating service response time for elastic cloud applications

Content Provider	IEEE Xplore Digital Library
Author	Salah, K. Boutaba, R.
Copyright Year	2012
Description	Author affiliation: David R. Cheriton School of Computer Science, University of Waterloo, Canada (Boutaba, R.) \|\| Electrical and Computer Engineering Department, Khalifa University of Science, Technology and Research, (KUSTAR), UAE (Salah, K.)
Abstract	This paper presents a Markovian analytical model to estimate service response time for elastic cloud applications. Given the expected application workload, the number of virtual machine (VM) instances, and the capacity of each VM instance, the model can approximate the mean service time. The mean service time is a critical metric to estimate, and contributes to the SLA end-to-end response time experienced by application users. The end-to-end response time is an aggregated delay of the service time in addition to delays incurred at the network nodes and links. Our analytical model focuses on estimating the mean service time; however, the model is sufficiently general and can be extremely useful in studying cloud performance. Equations for key performance measures are derived. These measures include mean response time, throughput, request loss, queueing probability, and CPU utilization. The correctness of the model has been verified using discrete-event simulation.
Starting Page	12
Ending Page	16
File Size	436638
Page Count	5
File Format	PDF
ISBN	9781467327978
e-ISBN	9781467327985
DOI	10.1109/CloudNet.2012.6483647
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-11-28
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cloud computing Computational modeling Conferences SLA Service and Network Delays Analytical models Performance Modeling and Analysis Cloud Computing Elastic Applications Queueing Theory Time factors Mathematical model Load modeling
Content Type	Text
Resource Type	Article

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