Uncertainty-Aware Online Time Series Multi-Step Forecasting Framework in Cloud Systems

Jiadong Chen, Yang Luo, Xiuqi Huang, Fuxin Jiang, Yangguang Shi, Tieying Zhang, Xiaofeng Gao

Abstract: Accurate resource planning in large-scale systems relies on reliable predictions of future workloads, a task inherently challenged by their variability and dynamism. Previous prediction methods are either ineffective to deal with the changing dynamics of the series, or are highly black-boxed and unable to conduct effective theoretical analysis. To address these issues, we design an effective ensemble framework, Interval Prediction with Online Chasing (IPOC), tailored for multi-step interval forecasting in real-time systems. Theoretically, by formulating the task as a Dynamic Deterministic Markov Decision Process (Dd-MDP), an advanced theoretical framework is introduced to analyze problem solvability and derive conditions for the existence of feasible solutions. Incorporating the proposed Adaptive Copula Conformal Inference (ACCI) module and a well-designed Chasing Oracle, IPOC captures the changing dynamics and temporal dependencies to enable multi-step forecasting. We organically integrate advanced online learning theories with time series forecasting tasks to construct a forecasting framework that is both theoretically rigorous and practically effective. Theoretical analysis underpins IPOC’s effectiveness, demonstrating sublinear regret and adherence to confidence interval specifications. The chasing regret of the Chasing Oracle is $O(L_c)$, and the overall regret of IPOC is $O(\sqrt{L_{c}T\log |\mathcal{F}|})$. Empirically, IPOC is validated through extensive experiments on five real-world datasets, including public datasets and different types of workload collected from Bytedance Cloud, with comparisons to 25 baselines and 4 forecasting horizons (1/5/10/30). Specifically, IPOC achieves an average reduction of over 20% in RMSE/MAE/SMAPE/ρ-risk compared to baselines across five datasets. Besides, we apply our model to a case study on predictive auto-scaling tasks in actual large-scale cloud systems to validate its utility.