Host load prediction in cloud computing with Discrete Wavelet Transformation (DWT) and Bidirectional Gated Recurrent Unit (BiGRU) network

Abstract

Providing pay-as-you-go storage and computing services have contributed to the widespread adoption of cloud computing. Using virtualization technology, cloud service providers can execute several instances on a single physical server, maximizing resource utilization. A challenging issue in cloud data centers is that available resources are rarely fully utilized. The server utilization rate is poor and often below 30%. An accurate host workload prediction enhances resource allocation resulting in more efficient resource utilization. Recently, numerous methods based on deep learning for predicting cloud computing workload have been developed. An efficient strategy must predict long-term dependencies on nonstationary host workload data and be quick enough to respond to incoming requests. This study employs a Bidirectional Gated-Recurrent Unit (BiGRU), Discrete Wavelet Transformation (DWT), and an attention mechanism to improve the host load prediction accuracy. DWT is used to decompose input data into sub-bands with different frequencies and to extract patterns from nonlinear and nonstationary data in order to improve prediction accuracy. The extracted features are fed into BiGRu to predict future workload. The attention mechanism is used in order to extract the temporal correlation features. This hybrid model was evaluated with cluster data sets from Google and Alibaba. Experimental results reveal that our method improves prediction accuracy by 3% to 56% compared to a variety of state-of-the-art methods.