  Elasticity is one of the key features of cloud computing that attracts many
SaaS providers to minimize their services' cost. Cost is minimized by
automatically provision and release computational resources depend on actual
computational needs. However, delay of starting up new virtual resources can
cause Service Level Agreement violation. Consequently, predicting cloud
resources provisioning gains a lot of attention to scale computational
resources in advance. However, most of current approaches do not consider
multi-seasonality in cloud workloads. This paper proposes cloud resource
provisioning prediction algorithm based on Holt-Winters exponential smoothing
method. The proposed algorithm extends Holt-Winters exponential smoothing
method to model cloud workload with multi-seasonal cycles. Prediction accuracy
of the proposed algorithm has been improved by employing Artificial Bee Colony
algorithm to optimize its parameters. Performance of the proposed algorithm has
been evaluated and compared with double and triple exponential smoothing
methods. Our results have shown that the proposed algorithm outperforms other
methods.
