Multiscale Representation Enhanced Temporal Flow Fusion Model for Long-Term Workload Forecasting

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 longterm dependencies.In particular, inconsistent performance between long-term history and near-term forecasts hinders long-range predictions.This paper proposes a novel framework leveraging selfsupervised multiscale representation learning to capture both longterm 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.