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Resource prediction based on double exponential smoothing in cloud computing

Authors: Jinhui Huang; Chunlin Li; Jie Yu;

Resource prediction based on double exponential smoothing in cloud computing

Abstract

With the development of cloud computing, customers are more and more concerned with cost on the resources which are not free in the cloud. Cloud resource providers can offer users two payment plans, i.e., reservation and on-demand plans for resource provision. In general, cost on resources gained by reservation plan is cheaper than on-demand plan. So the accuracy of resource prediction is of importance. In this paper, we present a resource prediction model based on double exponential smoothing, which considers not only the current state of resources but also the history records. Experiments performed on CloudSim cloud simulator show that the proposed method has a better performance on prediction accuracy.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
63
Top 10%
Top 10%
Top 10%
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