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A survey on workload prediction models in cloud based on spot instances for proactive auto scaling strategy
Secaran R.Y.,
Published in Innovare Academics Sciences Pvt. Ltd
Volume: 7
Issue: 4
Pages: 791 - 795
Auto scaling techniques help exploiting the elastic nature of cloud by provisioning resources on demand. Very often, the application hosted in a cloud tend to face workload surges which causes the application to respond slow or deny requests. There are reactive and proactive strategies for auto scaling to the rescue which helps provisioning instances based on the demand. When the demand is met after the workload surge the auto scaling is reactive in nature. Otherwise, the demand can be predicted beforehand to provision instances that serve during the surge is proactive in nature. Off late, the proactive auto scaling is gaining more attention amongst researchers. This survey presents a comprehensive detail about workload prediction in cloud and bidding for spot instances in cloud. The survey results uncover a possible concept which requires more attention and the one which helps tapping into resource heterogeneity of the spot market with a cost oriented benefit. © 2019 by Advance Scientific Research.
About the journal
JournalJournal of Critical Reviews
PublisherInnovare Academics Sciences Pvt. Ltd
Open AccessNo