The curvature similarity between a pair of the original series and its corresponding decomposed series is measured. Firstly, CEEMDAN is applied to decompose the CPU consumption data which is in the form of a time series. Therefore, a novel prediction approach called CEEMDAN-RIDGE that is centered on denoising is proposed and reported in this paper. In prior arts, denoising has not been considered as an approach to subside the prediction error. Noise in the energy consumption data is often a detrimental factor responsible for the CPU load prediction error. For energy saving, load balancing has been used but it is only effective when CPU loads are predicted accurately. Computer servers in cloud data centers are known to consume a huge amount of energy in their operations.
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