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AutoToken: Predicting Peak Parallelism for Big Data Analytics at Microsoft

Summary: AutoToken predicts peak resource usage for recurring big-data queries in serverless analytics. A lightweight, scalable predictor using multiple query-plan identifiers to detect recurring templates, integrated with Peregrine and validated on SCOPE jobs. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12212
Venue
VLDB
Year
2020
Pagerank
4.6796855e-05
Overall Rank
7,684 | 46.55%
DOI
10.14778/3415478.3415554

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 10 of 10 citing papers.

Rank Citing Paper Year Venue Pagerank
4,690 Deploying a Steered Query Optimizer in Production at Microsoft 2022 SIGMOD 5.997226e-05
4,804 Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload 2021 SIGMOD 5.910467e-05
6,040 Steering Query Optimizers: A Practical Take on Big Data Workloads 2021 SIGMOD 5.2412035e-05
6,209 AutoExecutor: Predictive Parallelism for Spark SQL Queries 2021 VLDB 5.1565972e-05
6,261 The Cosmos Big Data Platform at Microsoft: Over a Decade of Progress and a Decade to Look Forward 2021 VLDB 5.1350714e-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
8,220 PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost! 2021 VLDB 4.5557328e-05
8,416 Towards Building Autonomous Data Services on Azure 2023 SIGMOD 4.5196199e-05
10,932 Flux: Decoupled Auto-Scaling for Heterogeneous Query Workload in Alibaba AnalyticDB 2024 SIGMOD 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 7 of 7 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
953 Runtime Measurements in the Cloud: Observing, Analyzing, and Reducing Variance 2010 VLDB 0.00015095431
1,071 Starfish: A Self-tuning System for Big Data Analytics 2011 CIDR 0.00014312777
2,083 Towards a Learning Optimizer for Shared Clouds 2019 VLDB 9.5834572e-05
3,625 Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings 2020 SIGMOD 6.9055212e-05
4,174 Computation Reuse in Analytics Job Service at Microsoft 2018 SIGMOD 6.3856219e-05
9,735 SparkCruise: Handsfree Computation Reuse in Spark 2019 VLDB 4.2942813e-05
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3,625 Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings 2020 SIGMOD 6.9055212e-05
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6,209 AutoExecutor: Predictive Parallelism for Spark SQL Queries 2021 VLDB 5.1565972e-05