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Biathlon: Harnessing Model Resilience for Accelerating ML Inference Pipelines

Summary: Biathlon, an ML-serving system, exploits model resilience to input perturbations by selecting per-aggregation-feature approximation levels to maximize latency reduction while guaranteeing bounded end-to-end accuracy loss. Evaluated on real pipelines, it achieves 5.3x–16.6x speedups with negligible accuracy drop. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
h545ab1ee6c8e6be8
Venue
VLDB
Year
2024
Pagerank
5.402957e-05
Overall Rank
7,950 | 46.72%
DOI
10.14778/3675034.3675052
PDF
Download (CC BY-NC-ND 4.0)

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{chang_vldb24,
        title = {{Biathlon: Harnessing Model Resilience for Accelerating ML Inference Pipelines}},
        author = {Chang, Chaokun and Lo, Eric and Ye, Chunxiao},
        journal = {PVLDB},
        series = {{VLDB} '24},
        volume = {17},
        number = {10},
        pages = {2631--2640},
        doi = {10.14778/3675034.3675052},
        url = {https://doi.org/10.14778/3675034.3675052},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

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Outgoing Citations (Sorted by Pagerank)

Showing 27 of 27 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
9 Online Aggregation 1997 SIGMOD 0.00076079812
273 NoScope: Optimizing Neural Network Queries over Video at Scale 2017 VLDB 0.00022518449
320 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00021128745
594 Wander Join: Online Aggregation via Random Walks 2016 SIGMOD 0.00015814493
770 VerdictDB: Universalizing Approximate Query Processing 2018 SIGMOD 0.00014086699
841 Quickr: Lazily Approximating Complex AdHoc Queries in BigData Clusters 2016 SIGMOD 0.00013503124
1,435 Knowing When You’re Wrong: Building Fast and Reliable Approximate Query Processing Systems 2014 SIGMOD 0.00010659454
1,839 DBEst: Revisiting Approximate Query Processing Engines with Machine Learning Models 2019 SIGMOD 9.5187688e-05
1,874 G-OLA: Generalized On-Line Aggregation for Interactive Analysis on Big Data 2015 SIGMOD 9.4470874e-05
2,014 Sample + Seek: Approximating Aggregates with Distribution Precision Guarantee 2016 SIGMOD 9.1775869e-05
2,040 Database Learning: Toward a Database that Becomes Smarter Every Time 2017 SIGMOD 9.1319608e-05
2,199 Extending Relational Query Processing with ML Inference 2020 CIDR 8.8664379e-05
2,258 Evaluating End-to-End Optimization for Data Analytics Applications in Weld 2018 VLDB 8.7387151e-05
2,464 Query Processing on Tensor Computation Runtimes 2022 VLDB 8.4081954e-05
2,672 End-to-end Optimization of Machine Learning Prediction Queries 2022 SIGMOD 8.1301225e-05
2,682 Relational Confidence Bounds Are Easy With The Bootstrap* 2005 SIGMOD 8.1203255e-05
3,220 Turbo-Charging Estimate Convergence in DBO 2009 VLDB 7.5077984e-05
3,415 A Demonstration of Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference 2020 VLDB 7.3054307e-05
3,513 Serving and Optimizing Machine Learning Workflows on Heterogeneous Infrastructures 2023 VLDB 7.2253231e-05
4,198 Rafiki: Machine Learning as an Analytics Service System 2019 VLDB 6.7303243e-05
6,049 Incremental Computation of Common Windowed Holistic Aggregates 2016 VLDB 5.8866676e-05
6,249 Optimizing In-memory Database Engine for AI-powered On-line Decision Augmentation Using Persistent Memory 2021 VLDB 5.8190376e-05
6,428 Containerized Execution of UDFs: An Experimental Evaluation 2022 VLDB 5.7709339e-05
6,562 JoinBoost: Grow Trees Over Normalized Data Using Only SQL 2023 VLDB 5.7287508e-05
9,213 FEBench: A Benchmark for Real-Time Relational Data Feature Extraction 2023 VLDB 5.1926696e-05
10,150 Everest: A Top-K Deep Video Analytics System 2022 SIGMOD 5.0597449e-05
10,164 RALF: Accuracy-Aware Scheduling for Feature Store Maintenance 2024 VLDB 5.0570156e-05
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