Self-Adapting CPU Scheduling for Mixed Database Workloads via Hierarchical Deep Reinforcement Learning
Αποθηκεύτηκε σε:
| Εκδόθηκε σε: | Symmetry vol. 17, no. 7 (2025), p. 1109-1141 |
|---|---|
| Κύριος συγγραφέας: | |
| Άλλοι συγγραφείς: | , |
| Έκδοση: |
MDPI AG
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| Διαθέσιμο Online: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2073-8994 | ||
| 024 | 7 | |a 10.3390/sym17071109 |2 doi | |
| 035 | |a 3233253157 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231635 |2 nlm | ||
| 100 | 1 | |a Xing Suchuan |u Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA; sx80@alumni.duke.edu | |
| 245 | 1 | |a Self-Adapting CPU Scheduling for Mixed Database Workloads via Hierarchical Deep Reinforcement Learning | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Modern database systems require autonomous CPU scheduling frameworks that dynamically optimize resource allocation across heterogeneous workloads while maintaining strict performance guarantees. We present a novel hierarchical deep reinforcement learning framework augmented with graph neural networks to address CPU scheduling challenges in mixed database environments comprising Online Transaction Processing (OLTP), Online Analytical Processing (OLAP), vector processing, and background maintenance workloads. Our approach introduces three key innovations: first, a symmetric two-tier control architecture where a meta-controller allocates CPU budgets across workload categories using policy gradient methods while specialized sub-controllers optimize process-level resource allocation through continuous action spaces; second, graph neural network-based dependency modeling that captures complex inter-process relationships and communication patterns while preserving inherent symmetries in database architectures; and third, meta-learning integration with curiosity-driven exploration enabling rapid adaptation to previously unseen workload patterns without extensive retraining. The framework incorporates a multi-objective reward function balancing Service Level Objective (SLO) adherence, resource efficiency, symmetric fairness metrics, and system stability. Experimental evaluation through high-fidelity digital twin simulation and production deployment demonstrates substantial performance improvements: 43.5% reduction in p99 latency violations for OLTP workloads and 27.6% improvement in overall CPU utilization, with successful scaling to 10,000 concurrent processes maintaining sub-3% scheduling overhead. This work represents a significant advancement toward truly autonomous database resource management, establishing a foundation for next-generation self-optimizing database systems with implications extending to broader orchestration challenges in cloud-native architectures. | |
| 653 | |a Data base management systems | ||
| 653 | |a Deep learning | ||
| 653 | |a Communication | ||
| 653 | |a Optimization | ||
| 653 | |a Resource allocation | ||
| 653 | |a Decomposition | ||
| 653 | |a Transaction processing | ||
| 653 | |a Resource management | ||
| 653 | |a Workloads | ||
| 653 | |a Online analytical processing | ||
| 653 | |a Efficiency | ||
| 653 | |a Scheduling | ||
| 653 | |a Online transaction processing | ||
| 653 | |a Graph neural networks | ||
| 653 | |a Graph representations | ||
| 653 | |a Decision making | ||
| 653 | |a Neural networks | ||
| 653 | |a Network latency | ||
| 653 | |a Digital twins | ||
| 653 | |a Workload | ||
| 653 | |a Vector processing (computers) | ||
| 653 | |a Systems stability | ||
| 700 | 1 | |a Wang, Yihan |u School of Engineering and Applied Science, The University of Pennsylvania, Philadelphia, PA 19104, USA | |
| 700 | 1 | |a Liu Wenhe |u School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA; wenhel@cs.cmu.edu | |
| 773 | 0 | |t Symmetry |g vol. 17, no. 7 (2025), p. 1109-1141 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3233253157/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3233253157/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3233253157/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |