Risk-Aware TimeMixer with Asymmetric Upper-Bound Calibration for Cloud CPU Utilization Forecasting

Cloud central processing unit (CPU) utilization forecasting is fundamental to capacity planning, overload warning, elastic scaling, and resource provisioning in cloud computing systems. Conventional forecasting models usually optimize average point-error accuracy, whereas provisioning decisions are often more sensitive to high-load underestimation and upper-bound failures that indicate potential under-provisioning risk. This paper proposes Risk-Aware TimeMixer (RA-TimeMixer), a provisioning-oriented adaptation of Original TimeMixer for machine-level multi-step CPU utilization forecasting. RA-TimeMixer preserves the multiscale forecasting backbone and introduces two targeted risk-oriented components: batch-wise high-load weighted training and residual-based asymmetric upper-bound calibration. Experiments are conducted on a preprocessing-audited 50-machine subset of Alibaba Cluster Trace 2018 with 1 min sampling, input length 96, and prediction lengths 6, 12, and 24. At prediction length 12, RA-TimeMixer reduces High-load MAE, Under-rate high, Under-magnitude high, and Under-MAE when under by 2.72%, 1.89%, 4.06%, and 1.98%, respectively, compared with Original TimeMixer. Machine-level paired analyses, horizon and threshold studies, three-seed stability, persistence-baseline diagnostics, and fully observed-window retraining support the robustness of the observed accuracy–risk trade-off. The results indicate that RA-TimeMixer offers a transparent, risk-sensitive extension of TimeMixer for provisioning-oriented cloud CPU forecasting, while asymmetric calibration reduces empirical upper-bound violations at the cost of wider intervals and margins.