Adaptive Scheduling and Compute Demand Prediction for Animation Rendering Tasks Using Machine Learning

INTRODUCTION: The animation industry’s growing demand for high-resolution, multi-scene rendering has led to highly heterogeneous and dynamic workloads, challenging traditional scheduling systems. OBJECTIVES: This study aims to overcome the limitations of static or rule-based schedulers by developing an adaptive, learning-driven approach that dynamically matches rendering tasks to compute resources under fluctuating conditions. METHODS: We propose a machine learning-based scheduling and compute prediction model that integrates multi-dimensional task feature vectors with a lightweight sequence prediction network and a multi-objective optimization strategy. This enables real-time estimation of rendering duration, compute demand, and node load for dynamic resource allocation. The framework explicitly models temporal dependencies across frames and accounts for GPU heterogeneity through unified task representations. RESULTS: Evaluated on a cluster with 12 representative animation scene categories and 3 GPU architectures, our method shortens average task latency by 11.0%, improves node utilization by 3.9%, and reduces energy consumption by 7.8% over the best baseline, while maintaining robust performance even under severe (20%) state noise. CONCLUSION: The proposed model demonstrates strong adaptability and efficiency in complex rendering environments, offering a scalable foundation for intelligent, cross-platform scheduling in next-generation rendering infrastructures, particularly beneficial for cloud rendering, virtual production, and energy-constrained GPU clusters, while supporting real-time responsiveness and cost-effective resource management.