MSCNet: Multi-Scale Network With Convolutions for Long-Term Cloud Workload Prediction

Abstract

Accurate workload prediction is crucial for resource allocation and management in large-scale cloud data centers. While many approaches have been proposed, most existing methods are based on Recurrent Neural Networks (RNNs) or their variants, focusing on short-term cloud workload prediction without considering or identifying the long-term changes and different periodic patterns of cloud workloads. Due to variations in user demands or workload dynamics, cloud workloads that appear stable in the short term often exhibit distinct patterns in the long term. This can lead to a significant decline in prediction accuracy for existing methods when applied to long-term cloud workload forecasting. To address these challenges and overcome the limitations of current approaches, we propose a Multi-Scale Network with Convolutions (MSCNet) for accurate long-term cloud workload prediction. MSCNet employs multi-scale modeling of the original cloud workload to effectively extract multi-scale features and different periodic patterns, learning the long-term dependencies among the cloud workload. Our core component, the Multi-Scale Block, combines the Multi-Scale Patch Block, Transformer Encoder, and Multi-Scale Convolutions Block for comprehensive multi-scale learning. This enables MSCNet to adaptively learn both short-term and long-term features and patterns of cloud workloads, resulting in accurate long-term cloud workload predictions. Extensive experiments are conducted using real-world cloud workload data from Alibaba, Google, and Azure to validate the effectiveness of MSCNet. The experimental results demonstrate that MSCNet achieves accurate long-term cloud workload prediction with a computational complexity of $O(L^{2}d)$, outperforming existing state-of-the-art methods.