Oceanus: Enable SLO-Aware Vertical Autoscaling for Cloud-Native Streaming Services in Tencent
Zihao Chen, Jiazhi Jiang, Jiangang Liu, Chao Zhang, Yuqi Diao, Yang Li, Hanmei Luo, Peng Chen · 2025
Stream services have gained immense popularity for facilitating real-time data processing, powering numerous applications within Tencent. Most of these streaming jobs are deployed at scale on the cloud to achieve higher availability and stability. Due to the dynamic nature of input data rates, cloud-native streaming services within Tencent experience both periodical and distributional workload imbalances. Vertical scaling mechanism provided on the cloud platform aids in more fine-grained automatic resource adjustments for the workload imbalances. However, current vertical autoscaling solutions on cloud platform face challenges in achieving timely and accurate scaling predictions, managing service interruptions caused by resource scaling, and efficiently handling various autoscaling abnormalities. These issues can hinder the solutions from meeting the service level objectives (SLO) of streaming jobs in Tencent.