TFEGRU: Time-Frequency Enhanced Gated Recurrent Unit With Attention for Cloud Workload Prediction

Abstract

Accurate prediction of cloud workload is crucial for effective resource allocation in cloud computing. However, due to the complexity and high dimensionality of workloads in the cloud environment, achieving precise workload prediction is a complex and challenging problem. Current approaches to cloud workload prediction mainly rely on deep learning methods based on the Recurrent Neural Network (RNN), which struggle to capture the long-term dependencies inherent in workloads effectively. To tackle these challenges and overcome the limitations of existing methods, we propose an effective approach Time-Frequency Enhanced Gated Recurrent Unit with Attention (TFEGRU) for cloud workload prediction. First, we design a Time-Frequency Enhanced Block (TFEB) to capture complex workload patterns and extract features from both the frequency and temporal domains. Next, we integrate channel independent strategy and channel embedding into the model to adapt to high-dimensional workloads and enhance predictive performance. Finally, we apply a Gated Recurrent Unit (GRU) in conjunction with a multi-head self-attention mechanism to achieve accurate workload prediction. To validate the effectiveness of TFEGRU, comprehensive experiments are conducted using real-world traces from Google and Alibaba cloud data centers. The experimental results demonstrate that TFEGRU achieves accurate and efficient predictions across diverse cloud workloads, outperforming existing state-of-the-art methods.