Multiscale Representation Enhanced Temporal Flow Fusion Model for Long-Term Workload Forecasting
<|reference_start|>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 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.<|reference_end|>
@article{wang2024multiscale,
    title={Multiscale Representation Enhanced Temporal Flow Fusion Model for
  Long-Term Workload Forecasting},
    author={Shiyu Wang, Zhixuan Chu, Yinbo Sun, Yu Liu, Yuliang Guo, Yang Chen,
  Huiyang Jian, Lintao Ma, Xingyu Lu, Jun Zhou},
    journal={arXiv preprint arXiv:2407.19697},
    year={2024},
    doi={10.1145/3627673.3680072},
    archivePrefix={arXiv},
    eprint={2407.19697},
    primaryClass={cs.LG cs.AI}
}