Multiscale Representation Enhanced Temporal Flow Fusion Model for Long-Term Workload Forecasting
| Autor: | Wang, Shiyu, Chu, Zhixuan, Sun, Yinbo, Liu, Yu, Guo, Yuliang, Chen, Yang, Jian, Huiyang, Ma, Lintao, Lu, Xingyu, Zhou, Jun |
|---|---|
| Rok vydání: | 2024 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1145/3627673.3680072 |
| Popis: | 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. Comment: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM '24), October 21--25, 2024, Boise, ID, USA |
| Databáze: | arXiv |
| Externí odkaz: |
|