Accurate forecasting of container resource usage is crucial for efficient resource scheduling and ensuring Quality of Service (QoS) in cloud data centers. The inherent complexity of container workloads, characterized by strong temporal dependencies, multivariate correlations, and non-stationarity, challenges existing forecasting models, which often fail to efficiently capture both fine-grained local patterns and global trends. To address this gap, this paper proposes a novel Patch-based State-space Hybrid Network (PSH). PSH features a dual-branch architecture: a Local Transformer Path to model complex short-range dependencies and a Global Mamba Path, leveraging a State-Space Model (SSM) with linear-complexity, to efficiently capture long-range dependencies. This method uses an initial patching mechanism to reduce sequence length, which lowers computational overhead and supports efficient feature processing, and a cross-attention fusion module to integrate representations from its dual-branch architecture (Local Transformer Path for short-range dependencies, Global Mamba Path for long-range trends). The fusion module enables bidirectional interaction between the two paths: global context from the Global Mamba Path refines local features from the Local Transformer Path, balancing the model’s ability to capture both local patterns and global trends while maintaining high computational efficiency. Extensive experiments on the large-scale, real-world Alibaba Cluster Traces 2018 dataset demonstrate that PSH significantly outperforms existing state-of-the-art forecasting models in terms of accuracy and robustness.
Song et al. (Wed,) studied this question.