Lorentz: Learned SKU Recommendation Using Profile Data (DMDS)

Summary: Lorentz learns SKU/capacity recommendations for new cloud services without workload traces, using customer profile telemetry from existing users to predict provisioning needs. Novelty is a continuous feedback loop from satisfaction signals that personalizes cost-vs-performance choices, cutting slack >60% on Azure PostgreSQL VMs. (summarized by gpt-5.4-mini on May 24 2026)

Paper ID: 6913
Venue: SIGMOD
Year: 2024
Pagerank: 4.1945683e-05
Overall Rank: 10,966 | 23.72%
DOI: 10.1145/3654952

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Rank	Citing Paper	Year	Venue	Pagerank

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Showing 9 of 9 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
874	Index Selection in a Self-Adaptive Data Base Management System	1976	SIGMOD	0.00015728533
1,084	Dhalion: Self-Regulating Stream Processing in Heron	2017	VLDB	0.00014209714
1,322	Automated Demand-driven Resource Scaling in Relational Database-as-a-Service	2016	SIGMOD	0.00012610912
1,501	P-Store: An Elastic Database System with Predictive Provisioning	2018	SIGMOD	0.00011664869
2,375	Moneyball: Proactive Auto-Scaling in Microsoft Azure SQL Database Serverless	2022	VLDB	8.9452359e-05
6,110	Doppler: Automated SKU Recommendation in Migrating SQL Workloads to the Cloud	2022	VLDB	5.2056003e-05
6,757	KEA: Tuning an Exabyte-Scale Data Infrastructure	2021	SIGMOD	4.9372134e-05
7,047	Seagull: An Infrastructure for Load Prediction and Optimized Resource Allocation	2021	VLDB	4.8521181e-05
8,416	Towards Building Autonomous Data Services on Azure	2023	SIGMOD	4.5196199e-05

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