PRPOS: A Periodicity-Aware Resource Prediction Framework for Online Services

Accurate prediction of resource utilization is essential for efficient cloud resource management and Quality-of-Service (QoS) assurance in online services. However, most existing methods neglect to explicitly model inherent periodic patterns in resource usage—particularly those characterized by extended period lengths, consistent trend shapes with significant magnitude variations across periods—which limits their predictive accuracy. To address this gap, we propose PRPOS (Periodicity-aware Resource Prediction for Online Services), a novel periodicity-aware prediction framework specifically designed for online service workloads. PRPOS operates in two cohesive phases: It first employs a robust period detection mechanism that effectively handles magnitude variations and noise to identify dominant periods; then, a dual scale based on a Gated Recurrent Units (GRU) predictor explicitly incorporates the identified periodicity to concurrently model fine-grained in-period dynamics and coarse-grained cross-period trends. Extensive evaluation on the Alibaba Cluster Trace v2018 demonstrates that PRPOS consistently outperforms state-of-the-art approaches, achieving average improvements of 45.3% in Mean Absolute Percentage Error (MAPE) and 44.3% in Root Mean Squared Error (RMSE). The demonstrated performance enables the application of PRPOS to cloud resource orchestration for online services, allowing for proactive resource provisioning that enhances both efficiency and reliability.