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Self-supervised multi-scale cloud workload prediction with time series data augmentation
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DOI：10.1016/j.neunet.2026.109034.png)
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Abstract
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Accurate cloud workload forecasting is critical for proactive resource provisioning, cost control, and Service Level Agreement (SLA) compliance; however, it is hindered by the scarcity and heterogeneity of labels. We present Time Series Augmentation for Multi-Scale Prediction (TSA-MSP), a self-supervised framework that achieves near-fully supervised accuracy with limited labels. Conceptually, TSA-MSP couples cloud-informed augmentations-multiscale time warping, frequency-domain mixing, and periodic pattern injection-with a hierarchical multi-scale contrastive objective and efficient fine-tuning using lightweight adapters to capture short-and long-range workload patterns. We conducted an empirical experimental study on real-world traces (Alibaba 2018, Google 2019, Azure 2019): self-supervised pre-training on unlabeled data followed by fine-tuning with small labeled subsets. We evaluated the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Symmetric Mean Absolute Percentage Error (sMAPE), and a peak-oriented metric (Peak Prediction Accuracy, PPA) and performed ablations (augmentations, scales), label efficiency, and cross-dataset transfer tests. Remarkably, with only 20% labels, TSA-MSP attains RMSE within-1.5-1.7% of fully supervised training (100% labels) across all three datasets; for example, on Alibaba, it achieves MAE/RMSE 0.241/0.335 versus 0.238/0.330 for a fully supervised Path-former. Against the best self-supervised baseline at 20% labels, TSA-MSP reduces RMSE by-6.6-6.9%. In a 5%-label setting, it outperformed supervised training from scratch by-23-26% RMSE. Cross-dataset transfer further improves the RMSE by-2.4-5.2% over direct target-only pre-training. By combining domain-aware augmentations with multi-scale self-supervision and efficient adaptation, TSA-MSP delivers accurate forecasts under scarce labels, improving peak readiness while enabling faster, more cost-effective deployment for real-world cloud resource management.
Keywords:
Self-supervised learning
Data augmentation
Multi-scale prediction
Cloud workload
Contrastive learning
Time series forecasting
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