Oceanus: Enable SLO-Aware Vertical Autoscaling for Cloud-Native Streaming Services in Tencent

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.

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Published
2025-06-17
DOI
https://doi.org/10.1145/3722212.3724445
Primary Topic
Peer-to-Peer Network Technologies
Type
article
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Oceanus: Enable SLO-Aware Vertical Autoscaling for Cloud-Native Streaming Services in Tencent

J Liu, Zihao Chen, Jiazhi Jiang, Hanmei Luo et al.
Peer-to-Peer Network Technologies
article

Oceanus: Enable SLO-Aware Vertical Autoscaling for Cloud-Native Streaming Services in Tencent

J Liu, Zihao Chen, Jiazhi Jiang, Hanmei Luo, Peng Chen, Yang Li, Chao Zhang
article en

Abstract

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.

Tencent (China) (CN), Beijing Normal University (CN)
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Openalex Percentile: Top 85%
Peer-to-Peer Network Technologies
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