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

• URL: http://arxiv.org/abs/2407.19697v2
• Date: Mon, 19 Aug 2024 02:13:57 GMT
• Title: Multiscale Representation Enhanced Temporal Flow Fusion Model for Long-Term Workload Forecasting
• Authors: Shiyu Wang, Zhixuan Chu, Yinbo Sun, Yu Liu, Yuliang Guo, Yang Chen, Huiyang Jian, Lintao Ma, Xingyu Lu, Jun Zhou,
• Abstract summary: 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.
• Score: 19.426131129034115
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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