JointScaler: A Hierarchical Multi-Indicator Distribution Forecasting Approach for Uncertainty-Aware Joint Scaling in Cloud Services

Luo, Yang; Yu, Zhemeng; Fu, Yikang; LU, Wei; Ma, Lintao; Gao, Xiaofeng; Chen, Guihai

doi:10.24963/ijcai.2026/323

JointScaler: A Hierarchical Multi-Indicator Distribution Forecasting Approach for Uncertainty-Aware Joint Scaling in Cloud Services

Yang Luo, Zhemeng Yu, Yikang Fu, Wei Lu, Lintao Ma, Xiaofeng Gao, Guihai Chen

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Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. Main Track. Pages 2906-2914.

https://doi.org/10.24963/ijcai.2026/323

Keywords

Data Mining: Applications
Data Mining: Mining spatial and/or temporal data
Data Mining: Parallel, distributed and cloud-based high performance mining

Abstract

Proactive scaling improves cloud resource efficiency by forecasting system-relevant indicators and dynamically provisioning resources to maximize utilization while satisfying quality requirements. Existing approaches forecast service indicators in isolation, ignore forecasting uncertainty, and scale resource types independently, violating bundled resource constraints and degrading service quality. Therefore, we propose JointScaler, a learning-based framework for multi-indicator distribution forecasting and uncertainty-aware scaling. It captures inter-indicator dependencies via hierarchical attention, models dynamic uncertainties with normalizing flows, and leverages full predictive distributions to optimize bundled resource allocations under quality requirements. Evaluated on 4 real-world datasets, JointScaler improves point and distribution forecasting accuracy by 5.87% and 14.74%, outperforming 12 advanced baselines. In a week-long A/B test on a payment application’s cloud platform, it reduced GPU and CPU usage by 2,400+ and 37,000+ hours with stable service quality, delivering significant economic benefits.

Yang Luo, Zhemeng Yu, Yikang Fu, Wei LU, Lintao Ma, Xiaofeng Gao, and Guihai Chen. 2026. JointScaler: A Hierarchical Multi-Indicator Distribution Forecasting Approach for Uncertainty-Aware Joint Scaling in Cloud Services. In Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26. IJCAI, 2906–2914. https://doi.org/10.24963/ijcai.2026/323

@inproceedings{ijcai2026p323,
  title     = {JointScaler: A Hierarchical Multi-Indicator Distribution Forecasting Approach for Uncertainty-Aware Joint Scaling in Cloud Services},
  author    = {Luo, Yang and Yu, Zhemeng and Fu, Yikang and LU, Wei and Ma, Lintao and Gao, Xiaofeng and Chen, Guihai},
  booktitle = {Proceedings of the Thirty-Fifth International Joint Conference on
               Artificial Intelligence, {IJCAI-26}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Diego Calvanese},
  pages     = {2906--2914},
  year      = {2026},
  month     = {8},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2026/323},
  url       = {https://doi.org/10.24963/ijcai.2026/323},
}

TY  - CPAPER
TI  - JointScaler: A Hierarchical Multi-Indicator Distribution Forecasting Approach for Uncertainty-Aware Joint Scaling in Cloud Services
AU  - Luo, Yang
AU  - Yu, Zhemeng
AU  - Fu, Yikang
AU  - LU, Wei
AU  - Ma, Lintao
AU  - Gao, Xiaofeng
AU  - Chen, Guihai
T2  - Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2026
SP  - 2906
EP  - 2914
DO  - 10.24963/ijcai.2026/323
UR  - https://doi.org/10.24963/ijcai.2026/323
ER  -