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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
12425
Venue
VLDB
Year
2021
Pagerank
4.3761777e-05
Overall Rank
9,194 | 36.04%
DOI
10.14778/3476249.3476298

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 8 of 8 citing papers.

Rank Citing Paper Year Venue Pagerank
4,690 Deploying a Steered Query Optimizer in Production at Microsoft 2022 SIGMOD 5.997226e-05
5,368 Fine-Grained Modeling and Optimization for Intelligent Resource Management in Big Data Processing 2022 VLDB 5.5457532e-05
7,655 Machine Learning for Cloud Data Systems: the Progress so far and the Path Forward 2021 VLDB 4.6872456e-05
7,778 Runtime Variation in Big Data Analytics 2023 SIGMOD 4.653651e-05
8,416 Towards Building Autonomous Data Services on Azure 2023 SIGMOD 4.5196199e-05
8,854 Optimizing the cloud? Don't train models. Build oracles! 2024 CIDR 4.4349047e-05
8,859 Pipemizer: An Optimizer for Analytics Data Pipelines 2022 VLDB 4.4344107e-05
10,414 Rockhopper: A Robust Optimizer for Spark Configuration Tuning in Production Environment 2025 SIGMOD 4.1945683e-05
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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
22 SCOPE: Easy and Efficient Parallel Processing of Massive Data Sets 2008 VLDB 0.0008456613
70 Hive - A Warehousing Solution Over a Map-Reduce Framework 2009 VLDB 0.00059533166
204 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00034784455
333 Neo: A Learned Query Optimizer 2019 VLDB 0.00027206884
608 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00019235898
629 Preventing Bad Plans by Bounding the Impact of Cardinality Estimation Errors 2009 VLDB 0.00018942366
884 Plan-Structured Deep Neural Network Models for Query Performance Prediction 2019 VLDB 0.00015654004
1,226 Integrating Scale Out and Fault Tolerance in Stream Processing using Operator State Management 2013 SIGMOD 0.00013180799
1,254 Selectivity Estimation for Range Predicates using Lightweight Models 2019 VLDB 0.00013027411
1,922 Selecting Subexpressions to Materialize at Datacenter Scale 2018 VLDB 0.00010082599
1,990 Fault-Tolerance in the Borealis Distributed Stream Processing System 2005 SIGMOD 9.8472819e-05
2,083 Towards a Learning Optimizer for Shared Clouds 2019 VLDB 9.5834572e-05
2,575 A Latency and Fault-Tolerance Optimizer for Online Parallel Query Plans 2011 SIGMOD 8.5133576e-05
3,038 Azure Data Lake Store: A Hyperscale Distributed File Service for Big Data Analytics 2017 SIGMOD 7.6717218e-05
3,625 Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings 2020 SIGMOD 6.9055212e-05
3,886 Fault-tolerant Stream Processing using a Distributed, Replicated File System 2008 VLDB 6.6661649e-05
4,174 Computation Reuse in Analytics Job Service at Microsoft 2018 SIGMOD 6.3856219e-05
6,673 Incorporating Super-Operators in Big-Data Query Optimizers 2020 VLDB 4.966799e-05
9,448 Cost-based Fault-tolerance for Parallel Data Processing 2015 SIGMOD 4.3401906e-05
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