Demo Data from VLDB 2026 / reviewers in the wild / expert
Jiawang Tong
dblp:295/3616
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Cloud and datacenter computing · 100% | |
| Databases, data mining, and information retrieval
1 paper |
Distributed and cloud data management · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Distributed and cloud data management › cloud database
disaggregated database architecture |
0.5 | 1 | 2021 | PolarDB Serverless: A Cloud Native Database for Disaggregated Data Centers · SIGMOD Conference 2021 |
Cloud and datacenter computing › cloud data management
cloud-native database |
0.5 | 1 | 2021 | PolarDB Serverless: A Cloud Native Database for Disaggregated Data Centers · SIGMOD Conference 2021 |
Cloud and datacenter computing
resource provisioning |
0.1 | 1 | 2021 | PolarDB Serverless: A Cloud Native Database for Disaggregated Data Centers · SIGMOD Conference 2021 |
Methods — techniques the papers use, named apart from their topics
optimistic locking · 1.0index-aware prefetching · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | PolarDB Serverless: A Cloud Native Database for Disaggregated Data Centersabstract\beginabstract The trend in the DBMS market is to migrate to the cloud for elasticity, high availability, and lower costs. The traditional, monolithic database architecture is difficult to meet these requirements. With the development of high-speed network and new memory technologies, disaggregated data center has become a reality: it decouples various components from monolithic servers into separated resource pools (e.g., compute, memory, and storage) and connects them through a high-speed network. The next generation cloud native databases should be designed for disaggregated data centers. In this paper, we describe the novel architecture of \name, which follows thedisaggregation design paradigm: the CPU resource on compute nodes is decoupled from remote memory pool and storage pool. Each resource pool grows or shrinks independently, providing \revon-demand provisoning at multiple dimensions while improving reliability. We also design our system to mitigate the inherent penalty brought by resource disaggregation, and introduce optimizations such as optimistic locking and index awared prefetching. Compared to the architecture that uses local resources, \name achieves better dynamic resource provisioning capabilities and 5.3 times faster failure recovery speed, while achieving comparable performance. \endabstract Wei Cao 0006, Yingqiang Zhang, Xinjun Yang, Feifei Li 0001, Sheng Wang 0011, Qingda Hu, Xuntao Cheng, Zongzhi Chen, Zhenjun Liu, Bo Wang 0114, Haiqing Sun, Zhushi Cheng, Yusong Gao, Songlu Cai, Yunyang Zhang, Jiawang Tong |
SIGMOD Conference | 23 |