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

TL;DR

This paper introduces a multiscale representation learning framework with temporal flow fusion and attention mechanisms to improve long-term workload forecasting accuracy in cloud computing, addressing non-stationary and nonlinear challenges.

Contribution

It presents a novel multiscale and flow-based model that effectively captures long-term and near-term workload patterns for better long-range predictions.

Findings

Outperforms existing methods on 9 benchmark datasets.

Effectively models non-Gaussian and nonlinear workload distributions.

Enhances long-term forecast accuracy in cloud resource management.

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.