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)
Incoming Non-self Citations Over Time
Authors
- 1. Nick Glaze (Microsoft)
- 2. Tria McNeely (Microsoft)
- 3. Yiwen Zhu (Microsoft)
- 4. Matthew Gleeson (Microsoft)
- 5. Helen Serr (Microsoft)
- 6. Rajeev Bhopi (Microsoft)
- 7. Subru Krishnan (Microsoft)
BibTeX Citation
@inproceedings{glaze_sigmod24,
title = {{Lorentz: Learned SKU Recommendation Using Profile Data (DMDS)}},
author = {Glaze, Nick and McNeely, Tria and Zhu, Yiwen and Gleeson, Matthew and Serr, Helen and Bhopi, Rajeev and Krishnan, Subru},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3654952},
url = {https://dl.acm.org/doi/10.1145/3654952},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,919 | ScaleSense: Cost-Intelligent Scaling Framework via Learned Resource Estimation in Alibaba AnalyticDB | 2026 | VLDB | 4.9793485e-05 |
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Outgoing Citations (Sorted by Pagerank)
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 |
|---|---|---|---|---|
| 867 | Index Selection in a Self-Adaptive Data Base Management System | 1976 | SIGMOD | 0.00013371725 |
| 1,067 | Automated Demand-driven Resource Scaling in Relational Database-as-a-Service | 2016 | SIGMOD | 0.0001219157 |
| 1,160 | P-Store: An Elastic Database System with Predictive Provisioning | 2018 | SIGMOD | 0.00011769228 |
| 1,233 | Dhalion: Self-Regulating Stream Processing in Heron | 2017 | VLDB | 0.00011405873 |
| 1,994 | Moneyball: Proactive Auto-Scaling in Microsoft Azure SQL Database Serverless | 2022 | VLDB | 9.2176321e-05 |
| 5,938 | Doppler: Automated SKU Recommendation in Migrating SQL Workloads to the Cloud | 2022 | VLDB | 5.9411704e-05 |
| 6,901 | Seagull: An Infrastructure for Load Prediction and Optimized Resource Allocation | 2021 | VLDB | 5.6520335e-05 |
| 7,084 | KEA: Tuning an Exabyte-Scale Data Infrastructure | 2021 | SIGMOD | 5.6029455e-05 |
| 8,352 | Towards Building Autonomous Data Services on Azure | 2023 | SIGMOD | 5.3488341e-05 |
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 10,122 | Eigen+: Memory Over-Subscription for Alibaba Cloud Databases | 2025 | SIGMOD |
| 2 | 5,389 | Flexible Resource Allocation for Relational Database-as-a-Service | 2023 | VLDB |
| 3 | 5,836 | Tenant Placement in Over-subscribed Database-as-a-Service Clusters | 2022 | VLDB |
| 4 | 4,059 | Database-Agnostic Workload Management | 2019 | CIDR |
| 5 | 7,095 | DLRover-RM: Resource Optimization for Deep Recommendation Models Training in the Cloud | 2024 | VLDB |
| 6 | 2,478 | Learning a Partitioning Advisor for Cloud Databases | 2020 | SIGMOD |
| 7 | 1,067 | Automated Demand-driven Resource Scaling in Relational Database-as-a-Service | 2016 | SIGMOD |
| 8 | 10,919 | ScaleSense: Cost-Intelligent Scaling Framework via Learned Resource Estimation in Alibaba AnalyticDB | 2026 | VLDB |
| 9 | 5,938 | Doppler: Automated SKU Recommendation in Migrating SQL Workloads to the Cloud | 2022 | VLDB |
| 10 | 5,087 | Releasing Cloud Databases from the Chains of Performance Prediction Models | 2017 | CIDR |