Multiscale Representation Enhanced Temporal Flow Fusion Model for Long-Term Workload Forecasting
This addresses resource management in cloud computing, but appears incremental as it builds on transformer-based forecasting models.
The paper tackles the problem of long-term workload forecasting in cloud computing by proposing a framework that uses self-supervised multiscale representation learning and temporal flow fusion, achieving superior performance over existing methods on 9 benchmarks.
Accurate workload forecasting is critical for efficient resource management in cloud computing systems, enabling effective scheduling and autoscaling. Despite recent advances with transformer-based forecasting models, challenges remain due to the non-stationary, nonlinear characteristics of workload time series and the long-term dependencies. In particular, inconsistent performance between long-term history and near-term forecasts hinders long-range predictions. This paper proposes a novel framework leveraging self-supervised multiscale representation learning to capture both long-term and near-term workload patterns. The long-term history is encoded through multiscale representations while the near-term observations are modeled via temporal flow fusion. These representations of different scales are fused using an attention mechanism and characterized with normalizing flows to handle non-Gaussian/non-linear distributions of time series. Extensive experiments on 9 benchmarks demonstrate superiority over existing methods.