The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions

Summary: Proto-X (Holon) jointly tunes multiple DBMS configuration spaces by embedding cross-space similarities into a high-dimensional model and synthesizing “proto-actions” to navigate the combined space, avoiding tuner coordination/local optima. Outperforms sequential tuners, improving PostgreSQL up to 53%. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 13550
Venue: VLDB
Year: 2024
Pagerank: 4.6049527e-05
Overall Rank: 8,003 | 44.38%
DOI: 10.14778/3681954.3682007

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 6 of 6 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
9,012	Hit the Gym: Accelerating Query Execution to Efficiently Bootstrap Behavior Models for Self-Driving Database Management Systems	2024	VLDB	4.4059413e-05
9,581	Low Rank Learning for Offline Query Optimization	2025	SIGMOD	4.3186744e-05
9,643	Rockhopper: A Robust Optimizer for Spark Configuration Tuning in Production Environment	2025	SIGMOD	4.3067693e-05
9,980	Survivorship Bias in Industrial Database Workloads	2026	CIDR	4.1905499e-05
10,217	This is Going to Sound Crazy, But What If We Used Large Language Models to Boost Automatic Database Tuning Algorithms By Leveraging Prior History? We Will Find Better Configurations More Quickly Than Retraining From Scratch!	2026	SIGMOD	4.1905499e-05
10,435	Automated Database Tuning vs. Human-Based Tuning in a Simulated Stressful Work Environment: A Demonstration of the Database Gym	2025	SIGMOD	4.1905499e-05

Outgoing Citations (Sorted by Pagerank)

Showing 17 of 67 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
5,931	FASTgres: Making Learned Query Optimizer Hinting Effective	2023	VLDB	5.2632167e-05
6,113	Doppler: Automated SKU Recommendation in Migrating SQL Workloads to the Cloud	2022	VLDB	5.2006495e-05
6,364	ISUM: Efficiently Compressing Large and Complex Workloads for Scalable Index Tuning	2022	SIGMOD	5.0895007e-05
6,374	Dear User-Defined Functions, Inlining isn't working out so great for us. Let's try batching to make our relationship work. Sincerely, SQL	2024	CIDR	5.0874998e-05
6,376	A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning	2023	SIGMOD	5.0861082e-05
6,639	Leveraging Query Logs and Machine Learning for Parametric Query Optimization	2022	VLDB	4.976781e-05
7,009	Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective	2024	VLDB	4.8597992e-05
7,044	Seagull: An Infrastructure for Load Prediction and Optimized Resource Allocation	2021	VLDB	4.8475963e-05
7,220	Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation	2023	SIGMOD	4.7926382e-05
7,459	Lachesis: Automatic Partitioning for UDF-Centric Analytics	2021	VLDB	4.7199075e-05
7,854	dbET: Execution Time Distribution-based Plan Selection	2023	SIGMOD	4.6306186e-05
8,036	Tastes Great! Less Filling! High Performance and Accurate Training Data Collection for Self-Driving Database Management Systems	2022	SIGMOD	4.5965825e-05
8,043	DISTILL: Low-Overhead Data-Driven Techniques for Filtering and Costing Indexes for Scalable Index Tuning	2022	VLDB	4.5954398e-05
8,095	Grep: A Graph Learning Based Database Partitioning System	2023	SIGMOD	4.5837691e-05
8,219	PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost!	2021	VLDB	4.551524e-05
8,411	The Case for Learned In-Memory Joins	2023	VLDB	4.5151296e-05
9,469	Database Gyms	2023	CIDR	4.3304872e-05

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