PolarKV: Tier Locally, Serve Globally- A KV Cache over Cloud Memory and Storage
Summary: PolarKV is a cloud-native KV cache pairing co-located memory and block-storage shards, using memory for bandwidth and storage for capacity. Shard-local tiering avoids costly cross-VM movement and cuts LLM inference TTFT by up to 80%. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Yingqiang Zhang (Alibaba; Zhejiang University)
- 2. Xinjun Yang (Alibaba)
- 3. Hao Chen (Alibaba)
- 4. Feifei Li (Alibaba)
- 5. Shuwen Wang (Alibaba)
- 6. Chuan Sun (Alibaba)
- 7. Sai Wu (Zhejiang University)
- 8. Ninglong Weng (Alibaba)
BibTeX Citation
@article{zhang_vldb26,
title = {{PolarKV: Tier Locally, Serve Globally- A KV Cache over Cloud Memory and Storage}},
author = {Zhang, Yingqiang and Yang, Xinjun and Chen, Hao and Li, Feifei and Wang, Shuwen and Sun, Chuan and Wu, Sai and Weng, Ninglong},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {12},
pages = {4480--4493},
doi = {10.14778/3827998.3828047},
url = {https://doi.org/10.14778/3827998.3828047},
year = {2026}
}
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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 |
|---|---|---|---|---|
| 1,343 | PolarDB Serverless: A Cloud Native Database for Disaggregated Data Centers | 2021 | SIGMOD | 0.00010963588 |
| 1,398 | SSD Bufferpool Extensions for Database Systems | 2010 | VLDB | 0.00010783386 |
| 2,839 | Turbocharging DBMS Buffer Pool Using SSDs | 2011 | SIGMOD | 7.9506131e-05 |
| 3,028 | Autoscaling Tiered Cloud Storage in Anna | 2019 | VLDB | 7.7377953e-05 |
| 5,161 | HotPrefix: Hotness-Aware KV Cache Scheduling for Efficient Prefix Sharing in LLM Inference Systems | 2026 | SIGMOD | 6.2478968e-05 |
| 5,185 | PolarDB-MP: A Multi-Primary Cloud-Native Database via Disaggregated Shared Memory | 2024 | SIGMOD | 6.238732e-05 |
| 6,473 | AlayaDB: The Data Foundation for Efficient and Effective Long-context LLM Inference | 2025 | SIGMOD | 5.7739763e-05 |
| 7,945 | Unlocking the Potential of CXL for Disaggregated Memory in Cloud-Native Databases | 2025 | SIGMOD | 5.4212704e-05 |
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