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Phoebe: A Learning-based Checkpoint Optimizer
Summary: Phoebe, a learning-based checkpoint optimizer, uses predictors (exec time, output size, start/end) to decompose plans and place checkpoints. Formulated as an integer program with a scalable heuristic, it minimizes hotspot storage and speeds restarts.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 12426
- Venue
- VLDB
- Year
- 2021
- Pagerank
- 4.3842765e-05
- Overall Rank
- 9,138 | 36.50%
- DOI
-
10.14778/3476249.3476298
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 8 of 8 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 19 of 19 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 22 |
SCOPE: Easy and Efficient Parallel Processing of Massive Data Sets |
2008 |
VLDB |
0.00084679526 |
| 70 |
Hive - A Warehousing Solution Over a Map-Reduce Framework |
2009 |
VLDB |
0.00059744625 |
| 203 |
Learned Cardinalities: Estimating Correlated Joins with Deep Learning |
2019 |
CIDR |
0.00034868567 |
| 329 |
Neo: A Learned Query Optimizer |
2019 |
VLDB |
0.00027301488 |
| 606 |
DeepDB: Learn from Data, not from Queries! |
2020 |
VLDB |
0.00019251186 |
| 627 |
Preventing Bad Plans by Bounding the Impact of Cardinality Estimation Errors |
2009 |
VLDB |
0.00018959896 |
| 876 |
Plan-Structured Deep Neural Network Models for Query Performance Prediction |
2019 |
VLDB |
0.00015660534 |
| 1,226 |
Integrating Scale Out and Fault Tolerance in Stream Processing using Operator State Management |
2013 |
SIGMOD |
0.00013168869 |
| 1,239 |
Selectivity Estimation for Range Predicates using Lightweight Models |
2019 |
VLDB |
0.00013091459 |
| 1,921 |
Selecting Subexpressions to Materialize at Datacenter Scale |
2018 |
VLDB |
0.00010085899 |
| 1,995 |
Fault-Tolerance in the Borealis Distributed Stream Processing System |
2005 |
SIGMOD |
9.83817e-05 |
| 2,080 |
Towards a Learning Optimizer for Shared Clouds |
2019 |
VLDB |
9.5954034e-05 |
| 2,578 |
A Latency and Fault-Tolerance Optimizer for Online Parallel Query Plans |
2011 |
SIGMOD |
8.5060604e-05 |
| 3,044 |
Azure Data Lake Store: A Hyperscale Distributed File Service for Big Data Analytics |
2017 |
SIGMOD |
7.6624689e-05 |
| 3,623 |
Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings |
2020 |
SIGMOD |
6.9017341e-05 |
| 3,887 |
Fault-tolerant Stream Processing using a Distributed, Replicated File System |
2008 |
VLDB |
6.6601639e-05 |
| 4,171 |
Computation Reuse in Analytics Job Service at Microsoft |
2018 |
SIGMOD |
6.3800823e-05 |
| 6,671 |
Incorporating Super-Operators in Big-Data Query Optimizers |
2020 |
VLDB |
4.9625353e-05 |
| 9,451 |
Cost-based Fault-tolerance for Parallel Data Processing |
2015 |
SIGMOD |
4.3364392e-05 |
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4.1905499e-05 |
| 3,623 |
Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings |
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SIGMOD |
6.9017341e-05 |
| 5,994 |
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SIGMOD |
5.2367998e-05 |