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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
h88ed67fdeba5227f
Venue
VLDB
Year
2021
Pagerank
5.1676892e-05
Overall Rank
9,394 | 37.04%
DOI
10.14778/3476249.3476298
PDF
Download (CC BY-NC-ND 4.0)

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{zhu_vldb21,
        title = {{Phoebe: A Learning-based Checkpoint Optimizer}},
        author = {Zhu, Yiwen and Interlandi, Matteo and Roy, Abhishek and Das, Krishnadhan and Patel, Hiren and Bag, Malay and Sharma, Hitesh and Jindal, Alekh},
        journal = {PVLDB},
        series = {{VLDB} '21},
        volume = {14},
        number = {11},
        pages = {2505--2518},
        doi = {10.14778/3476249.3476298},
        url = {https://doi.org/10.14778/3476249.3476298},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 8 of 8 citing papers.

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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
30 SCOPE: Easy and Efficient Parallel Processing of Massive Data Sets 2008 VLDB 0.00050364918
31 Hive - A Warehousing Solution Over a Map-Reduce Framework 2009 VLDB 0.00049707539
84 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035920657
142 Neo: A Learned Query Optimizer 2019 VLDB 0.00029114937
320 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00021128745
381 Preventing Bad Plans by Bounding the Impact of Cardinality Estimation Errors 2009 VLDB 0.00019497061
545 Plan-Structured Deep Neural Network Models for Query Performance Prediction 2019 VLDB 0.0001657599
690 Selectivity Estimation for Range Predicates using Lightweight Models 2019 VLDB 0.00014703258
973 Integrating Scale Out and Fault Tolerance in Stream Processing using Operator State Management 2013 SIGMOD 0.00012734154
1,436 Towards a Learning Optimizer for Shared Clouds 2019 VLDB 0.00010658836
1,757 Selecting Subexpressions to Materialize at Datacenter Scale 2018 VLDB 9.7050769e-05
1,841 Fault-Tolerance in the Borealis Distributed Stream Processing System 2005 SIGMOD 9.5155524e-05
2,452 Azure Data Lake Store: A Hyperscale Distributed File Service for Big Data Analytics 2017 SIGMOD 8.4280184e-05
2,684 A Latency and Fault-Tolerance Optimizer for Online Parallel Query Plans 2011 SIGMOD 8.1185673e-05
2,828 Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings 2020 SIGMOD 7.9476509e-05
3,550 Computation Reuse in Analytics Job Service at Microsoft 2018 SIGMOD 7.1895215e-05
3,801 Fault-tolerant Stream Processing using a Distributed, Replicated File System 2008 VLDB 6.9925771e-05
6,294 Incorporating Super-Operators in Big-Data Query Optimizers 2020 VLDB 5.8043081e-05
9,809 Cost-based Fault-tolerance for Parallel Data Processing 2015 SIGMOD 5.1125251e-05
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