Self-Tuning Query Scheduling for Analytical Workloads

Summary: Presents a lock-free, self-tuning stride scheduler for task-based analytics, replacing OS scheduling with adaptive control of priorities and task granularity. Incorporates domain knowledge to boost scheduling elasticity under concurrent workloads, delivering near-optimal latencies and 10x tail-latency gains over classic DB systems. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: hd1daa599ae1fa979
Venue: SIGMOD
Year: 2021
Pagerank: 6.6228033e-05
Overall Rank: 4,388 | 70.50%
DOI: 10.1145/3448016.3457260

Incoming Non-self Citations Over Time

Authors

1. Benjamin Wagner (Technical University of Munich)
2. André Kohn (Technical University of Munich)
3. Thomas Neumann (Technical University of Munich)

BibTeX Citation

@inproceedings{wagner_sigmod21,
        title = {{Self-Tuning Query Scheduling for Analytical Workloads}},
        author = {Wagner, Benjamin and Kohn, André and Neumann, Thomas},
        series = {{SIGMOD} '21},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3448016.3457260},
        url = {https://dl.acm.org/doi/10.1145/3448016.3457260},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 14 of 14 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
3,563	Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift	2023	SIGMOD	7.2042148e-05
5,041	LSched: A Workload-Aware Learned Query Scheduler for Analytical Database Systems	2022	SIGMOD	6.3006152e-05
5,214	Stage: Query Execution Time Prediction in Amazon Redshift	2024	SIGMOD	6.2248104e-05
6,166	Apt-Serve: Adaptive Request Scheduling on Hybrid Cache for Scalable LLM Inference Serving	2025	SIGMOD	5.8631131e-05
7,864	Blueprinting the Cloud: Unifying and Automatically Optimizing Cloud Data Infrastructures with BRAD	2024	VLDB	5.4367919e-05
8,948	Tao: Improving Resource Utilization while Guaranteeing SLO in Multi-tenant Relational Database-as-a-Service	2024	SIGMOD	5.252939e-05
9,008	Low-Latency Transaction Scheduling via Userspace Interrupts: Why Wait or Yield When You Can Preempt?	2025	SIGMOD	5.2387493e-05
9,316	On-Demand State Separation for Cloud Data Warehousing	2022	VLDB	5.1958405e-05
10,309	Intra-Query Runtime Elasticity for Cloud-Native Data Analysis	2025	SIGMOD	5.0386264e-05
10,666	P-MOSS: Scheduling Main-Memory Indexes Over NUMA Servers Using Next Token Prediction	2026	SIGMOD	4.9793485e-05
10,930	Seron: Smart Query Router for Multi-Primary Cloud-Native Databases with Shared Storage	2026	VLDB	4.9793485e-05
11,190	Moving on From Group Commit: Autonomous Commit Enables High Throughput and Low Latency on NVMe SSDs	2025	SIGMOD	4.9793485e-05
11,447	LASER: Buffer-Aware Learned Query Scheduling in Master-Standby Databases	2025	VLDB	4.9793485e-05
11,461	Oligolithic Cross-task Optimizations across Isolated Workloads*	2024	CIDR	4.9793485e-05

Outgoing Citations (Sorted by Pagerank)

Showing 6 of 6 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
21	Efficiently Compiling Efficient Query Plans for Modern Hardware	2011	VLDB	0.00056855599
215	Morsel-Driven Parallelism: A NUMA-Aware Query Evaluation Framework for the Many-Core Age	2014	SIGMOD	0.00024598661
373	Umbra: A Disk-Based System with In-Memory Performance	2020	CIDR	0.00019711632
555	SageDB: A Learned Database System	2019	CIDR	0.00016506678
3,047	Handling Highly Contended OLTP Workloads Using Fast Dynamic Partitioning	2020	SIGMOD	7.7124257e-05
4,055	Scaling Up Concurrent Main-Memory Column-Store Scans: Towards Adaptive NUMA-aware Data and Task Placement	2015	VLDB	6.8270333e-05

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10	5,041	LSched: A Workload-Aware Learned Query Scheduler for Analytical Database Systems	2022	SIGMOD