Transformer-based latency prediction for stream processing task - Zheng Chu; Baozhu Li; Changtian Ying (Journal of King Saud University: Computer and Information Sciences)
Abstract Latency prediction for stream processing tasks (SPTs) is a critical issue for stream computing, parameter tuning, load optimization, task scheduling, etc. This study addresses the real-time, volatile, and high-volume nature of streaming workloads to improve latency prediction accuracy. A novel model based on Auto-encoders and Transformers was proposed to address the above challenges. The Auto-encoder is utilized to reduce the dimensionality of the extensive features and generate a compressed feature representation. Subsequently, the Transformer is employed to extract spatio-temporal dependencies and predict the latency of SPTs. Experimental results on six benchmarks (about 260,000 samples in total) demonstrate that compared to state-of-the-art models, the proposed model achieves higher accuracy and lower error rates, with average errors of 0.11% (MAE), 0.02% (RMSE), respectively, and average accuracy of 0.89. Moreover, a time window length of 60 or 80 allows the model to achieve the best average accuracy. Additionally, the ablation study verified that the Transformer significantly improved the prediction performance. These results confirm that the proposed model can accurately predict latency for SPTs, further laying the foundation for intelligent and proactive optimization of stream processing systems (SPSs).