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All Journal Journal of Technology Informatics and Engineering
Siming Zhao
Business Analytics, Columbia University, NY, USA
Computer Science & IT

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Journal : journal of technology informatics and engineering

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Multi-Horizon GPU Demand Forecasting with Workload Semantics and Operational Risk Curves: An Empirical Study on Alibaba Clusterdata GPU Trace Siming Zhao; Jingwen Bai; Drew Roberson
Journal of Technology Informatics and Engineering Vol. 4 No. 3 (2025): DECEMBER | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i3.498

Abstract

This study addresses the operational challenge of multi-horizon GPU demand forecasting in large-scale computing clusters, where GPUs are costly resources and demand fluctuates under constraint-driven scheduling. The objective is to evaluate whether integrating workload semantics improves forecasting performance across horizons up to 72 hours. A reproducible empirical benchmark is developed using the Alibaba Clusterdata GPU trace (cluster-trace-gpu-v2023), comprising 8,152 pods over approximately 149 days with a total capacity of 6,212 GPUs. The study compares two statistical baselines, ARIMA(48,0,0) and a seasonal-trend additive model, with three lightweight deep learning models: Temporal Convolutional Network (TCN), Informer-lite, and TFT-lite. Workload semantics are approximated by converting hourly job metadata into textual summaries, embedding them with TF-IDF and truncated SVD (8 dimensions), and incorporating them as exogenous covariates. Evaluation uses SMAPE and MASE across multiple horizons (1–72 hours), along with peak-aware metrics and operational risk curves. Results show that the seasonal-trend model achieves the best overall accuracy (15.34% sMAPE), while TCN is the strongest deep model (17.20% sMAPE). Semantic embeddings do not improve short horizons (1–48 hours) but reduce 72-hour sMAPE by 11.1% and improve peak-window error. These findings indicate that autoregressive signals dominate short-term forecasting, whereas semantic context becomes beneficial at longer horizons. The study emphasizes that combining point accuracy with risk-based evaluation is essential for effective GPU capacity planning under dynamic and uncertain demand conditions.
Profit-Aware Spot GPU Admission Control with Cost-Sensitive Loss and Evidence-Grounded Policy Memos for AI Workload Supply-Demand Matching Siming Zhao; Yuxuan Ren; Xiaohan Chang
Journal of Technology Informatics and Engineering Vol. 5 No. 2 (2026): AUGUST | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v5i2.545

Abstract

AI clusters increasingly operate with heterogeneous GPU resources where production workloads and opportunistic spot jobs compete for limited accelerator capacity. This study presents a trace-driven admission-control framework using the Alibaba cluster-trace-v2026-spot-gpu dataset, consisting of 466,867 job records and 4,278 GPU-node records. The experiment evaluates GPU demand forecasting, profit-aware spot admission control, and evidence-grounded operational policy generation using chronological training, validation, and test splits. Hourly spot GPU demand forecasting was evaluated across six GPU models, where Ridge regression achieved the best test performance with an RMSE of 38.50 requested GPUs per hour, improving over both last-hour and seasonal naive baselines. The admission-control evaluation compared FIFO, greedy packing, classifier-based acceptance, utility ranking, and the proposed cost-sensitive policy. The proposed approach achieved a test profit of 67,278.96, improving 1.97% over the accuracy-oriented classifier while increasing spot success rate and reducing costly false acceptances by 13.17%. Sensitivity analysis showed that the optimal policy depends on the protection cost assigned to high-priority workloads. A deterministic evidence-grounded explanation layer generated 500 policy memos and passed numeric, policy, and evidence consistency checks. The findings suggest that profit-aware admission control can serve as a practical scheduling guardrail before detailed GPU placement and resource allocation decisions.
Co-Authors Drew Roberson Jingwen Bai Xiaohan Chang Yuxuan Ren