Using Multiple Seasonal Holt-Winters Exponential Smoothing to Predict Cloud Resource Provisioning

TL;DR

This paper introduces a multi-seasonal Holt-Winters exponential smoothing algorithm, enhanced with Artificial Bee Colony optimization, to improve cloud resource provisioning predictions considering complex workload patterns.

Contribution

It extends Holt-Winters method to model multi-seasonal cloud workloads and optimizes parameters with Artificial Bee Colony algorithm for better accuracy.

Findings

Proposed method outperforms double and triple exponential smoothing.

Incorporating multi-seasonality improves prediction accuracy.

Optimization enhances model performance in cloud resource forecasting.

Abstract

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.