What does this research mean for the field?

DEL4CW significantly improves the accuracy of cloud workload predictions, achieving error reductions of up to 27.74% compared to existing methods. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The aim is to enhance cloud workload prediction accuracy by addressing instability and inefficiency in time series methods.

February 21, 2026

DEL4CW: Deep Expansion Learning for Cloud Workloads Prediction

Key Points

The aim is to enhance cloud workload prediction accuracy by addressing instability and inefficiency in time series methods.
Developed a deep expansion learning framework tailored for cloud workload prediction.
Implemented a self-decoupling mechanism to separate complex workload dependencies.
Utilized stacked blocks for trend, periodicity, and compensation modules to manage variable workloads.
Achieved up to 27.74% reduction in prediction error compared to traditional methods.
Demonstrated improved short-term fluctuation management and long-term trend prediction.
Provided interpretable insights through hierarchical prediction aggregation.

Abstract

Cloud workload prediction (CWP) is a critical task in cloud computing, essential for resource scheduling, performance optimization, and cost management. However, existing time series prediction methods struggle with instability and inefficiency when applied directly to cloud workloads due to their high variability and frequent fluctuations. To address these challenges, we propose DEL4CW, a novel D eep E xpansion L earning framework specifically designed for C loud W orkload prediction. DEL4CW introduces a unique self-decoupling mechanism to disentangle the complex dependencies present in highly variable cloud workloads, leading to more accurate predictions of job arrival rates. The core contribution of DEL4CW lies in its ability to decouple cloud workload signals into three key components—trend, periodicity, and residuals—by treating these as hidden variables. This enables the model to better manage both short-term fluctuations and long-term workload trends. DEL4CW employs a deep expansion learning framework structured as stacked blocks, where each block includes dedicated modules for trend, periodicity, and compensation. Specifically, the trend module utilizes multi-layer fully connected networks to capture evolving trends at multiple granularities, while the periodicity module leverages multi-head attention to identify diverse periodic patterns. The compensation module addresses unpredictable, localized fluctuations, improving the model’s robustness to noise. In addition to its predictive accuracy, DEL4CW provides interpretable insights through its hierarchical design, allowing for layer-by-layer aggregation of meaningful partial predictions. This interpretability stems from the doubly residual learning pipeline, which ensures that each prediction block contributes progressively refined predictions. Extensive experiments on real-world cloud workload traces demonstrate that DEL4CW significantly outperforms existing baselines, with error reductions reaching up to 27.74% in certain scenarios.

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