Oceanus: Enable SLO-Aware Vertical Autoscaling for Cloud-Native Streaming Services in Tencent
Summary: Oceanus enables SLO-aware vertical autoscaling for Tencent's cloud-native streaming workloads. ML-driven forecasting, cross-layer in-situ resource adjustment without interruption, and a robust failure-resolution workflow ensure precise scaling and rapid recovery in production. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Zihao Chen (Tencent)
- 2. Jiazhi Jiang (Beijing Normal University)
- 3. Jiangang Liu (Tencent)
- 4. Chao Zhang (Tencent)
- 5. Yuqi Diao (Tencent)
- 6. Yang Li (Tencent)
- 7. Hanmei Luo (Tencent)
- 8. Peng Chen (Tencent)
BibTeX Citation
@inproceedings{chen_sigmod25,
title = {{Oceanus: Enable SLO-Aware Vertical Autoscaling for Cloud-Native Streaming Services in Tencent}},
author = {Chen, Zihao and Jiang, Jiazhi and Liu, Jiangang and Zhang, Chao and Diao, Yuqi and Li, Yang and Luo, Hanmei and Chen, Peng},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3722212.3724445},
url = {https://dl.acm.org/doi/10.1145/3722212.3724445},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,583 | Resilience-Aware Elastic Scaling for Cloud-Native Online DL Training on Multi-Tenant GPU Clusters | 2026 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 8 of 8 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 220 | Storm @Twitter | 2014 | SIGMOD | 0.00024244587 |
| 224 | MillWheel: Fault-Tolerant Stream Processing at Internet Scale | 2013 | VLDB | 0.00024130894 |
| 361 | The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing | 2015 | VLDB | 0.00020138717 |
| 1,092 | Automated Demand-driven Resource Scaling in Relational Database-as-a-Service | 2016 | SIGMOD | 0.00012221946 |
| 3,950 | MagicScaler: Uncertainty-aware, Predictive Autoscaling | 2023 | VLDB | 6.9980354e-05 |
| 5,821 | StreamOps: Cloud-Native Runtime Management for Streaming Services in ByteDance | 2023 | VLDB | 6.07598e-05 |
| 6,781 | Seagull: An Infrastructure for Load Prediction and Optimized Resource Allocation | 2021 | VLDB | 5.7764885e-05 |
| 8,910 | Vertically Autoscaling Monolithic Applications with CaaSPER: Scalable Container-as-a-Service Performance Enhanced Resizing Algorithm for the Cloud | 2024 | SIGMOD | 5.3483178e-05 |
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