EASE: Resource-aware Query Scheduling across Heterogeneous Cloud Compute Services

Summary: EASE schedules OLAP queries across VMs, FaaS, and query services using a heterogeneity-aware cost model and real-time resource-aware scheduling. It dynamically migrates queries to handle runtime volatility, cutting latency by up to 35% and cost by 67%. (summarized by gpt-6-luna on Oct 22 2026)

Paper ID: h262498b4b218ef9b
Venue: SIGMOD
Year: 2027
Pagerank: 4.9605343e-05
Overall Rank: 10,393 | 30.34%
DOI: 10.1145/3837108
PDF: Download (CC BY 4.0)

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

1. Wenbo Li (Tsinghua University)
2. Haoqiong Bian (Renmin University of China)
3. Chao Zhang (Renmin University of China)
4. Guoliang Li (Tsinghua University)

BibTeX Citation

@inproceedings{li_sigmod27,
        title = {{EASE: Resource-aware Query Scheduling across Heterogeneous Cloud Compute Services}},
        author = {Li, Wenbo and Bian, Haoqiong and Zhang, Chao and Li, Guoliang},
        series = {{SIGMOD} '27},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3837108},
        url = {https://dl.acm.org/doi/10.1145/3837108},
        year = {2027}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 24 of 24 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
15	How Good Are Query Optimizers, Really?	2016	VLDB	0.00061463175
355	Bao: Making Learned Query Optimization Practical	2021	SIGMOD	0.00020146245
453	An End-to-End Learning-based Cost Estimator	2020	VLDB	0.0001801482
545	Plan-Structured Deep Neural Network Models for Query Performance Prediction	2019	VLDB	0.0001657599
651	Amazon Redshift Re-invented	2022	SIGMOD	0.00015126716
913	Starling: A Scalable Query Engine on Cloud Functions	2020	SIGMOD	0.00013084137
1,716	Lambada: Interactive Data Analytics on Cold Data Using Serverless Cloud Infrastructure	2020	SIGMOD	9.7923765e-05
2,169	QueryFormer: A Tree Transformer Model for Query Plan Representation	2022	VLDB	8.9151935e-05
2,619	Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction	2022	VLDB	8.1985665e-05
2,892	WiSeDB: A Learning-based Workload Management Advisor for Cloud Databases	2016	VLDB	7.8872299e-05
3,193	Vexless: A Serverless Vector Data Management System Using Cloud Functions	2024	SIGMOD	7.5325527e-05
3,328	Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift	2023	SIGMOD	7.4094448e-05
3,384	Towards Predicting Query Execution Time for Concurrent and Dynamic Database Workloads	2013	VLDB	7.3392425e-05
3,998	Resource Management in Aurora Serverless	2024	VLDB	6.8497732e-05
4,096	Self-Tuning Query Scheduling for Analytical Workloads	2021	SIGMOD	6.7949494e-05
4,343	Intelligent Scaling in Amazon Redshift	2024	SIGMOD	6.6397106e-05
4,999	Using Cloud Functions as Accelerator for Elastic Data Analytics	2023	SIGMOD	6.3086259e-05
6,618	Cackle: Analytical Workload Cost and Performance Stability With Elastic Pools	2023	SIGMOD	5.715013e-05
7,363	Blueprinting the Cloud: Unifying and Automatically Optimizing Cloud Data Infrastructures with BRAD	2024	VLDB	5.5279826e-05
9,011	Improving DBMS Scheduling Decisions with Accurate Performance Prediction on Concurrent Queries	2025	VLDB	5.2238303e-05
9,243	CloudGlide: Deconstructing the Landscape of Cloud-Based Analytics	2025	VLDB	5.1864799e-05
9,476	Saving Money for Analytical Workloads in the Cloud	2024	VLDB	5.157794e-05
10,092	Managed Resource Scaling in Amazon EMR	2025	SIGMOD	5.068372e-05
11,505	LASER: Buffer-Aware Learned Query Scheduling in Master-Standby Databases	2025	VLDB	4.9605343e-05

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