MLOS in Action: Bridging the Gap Between Experimentation and Auto-Tuning in the Cloud
Summary: MLOS enables one‑click cloud benchmarking and multi‑VM experimentation with integrated metrics collection and lightweight data storage/visualization for notebook-driven workflows. Provides pluggable ML/heuristic optimizers to auto‑tune VM/OS/kernel/userland parameters, bridging practical experimentation and automated configuration search. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Brian Kroth
- 2. Sergiy Matusevych
- 3. Rana Alotaibi
- 4. Yiwen Zhu
- 5. Anja Gruenheid
- 6. Yuanyuan Tian
Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,582 | Towards Query Optimizer as a Service (QOaaS) in a Unified LakeHouse Ecosystem: Can One QO Rule Them All? | 2025 | CIDR | 4.492033e-05 |
| 9,232 | AutoComp: Automated Data Compaction for Log-Structured Tables in Data Lakes | 2025 | SIGMOD | 4.3690661e-05 |
| 10,225 | LIO: A lightweight and interpretable query optimizer based on an evolutionary forest | 2026 | VLDB | 4.1945683e-05 |
| 10,414 | Rockhopper: A Robust Optimizer for Spark Configuration Tuning in Production Environment | 2025 | SIGMOD | 4.1945683e-05 |
| 10,849 | AXE: A Task Decomposition Approach to Learned LSM Tuning | 2025 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 11 of 11 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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