
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.
| 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). | 63 | |
| 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. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
