Intelligent Transaction Scheduling to Enhance Concurrency in High-Contention Workloads
Concurrency control (CC) scheme based on transaction decomposition has significantly enhanced the concurrency performance of multicore in-memory databases, surpassing traditional CC schemes such as two-phase locking (2PL) or optimistic concurrency control (OCC), particularly in high-contention scena...
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| Veröffentlicht in: | Applied sciences Jg. 15; H. 11; S. 6341 |
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| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Basel
MDPI AG
01.06.2025
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| Schlagworte: | |
| ISSN: | 2076-3417, 2076-3417 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | Concurrency control (CC) scheme based on transaction decomposition has significantly enhanced the concurrency performance of multicore in-memory databases, surpassing traditional CC schemes such as two-phase locking (2PL) or optimistic concurrency control (OCC), particularly in high-contention scenarios. However, this performance improvement introduces new challenges, as balancing transaction dependency constraints with enhanced concurrency optimization remains a persistent issue, especially with the increased number of concurrent client requests, which can lead to complex transaction dependencies. To address these challenges, we propose Dynamic Contention Scheduling (DCoS), a novel method that enhances transaction concurrency via a dual-granularity architecture. DCoS integrates a deep reinforcement learning (DRL)-based executor to schedule high-contention transactions while preserving dependency correctness. DCoS employs a one-shot execution model that enables fine-grained scheduling in high-contention scenarios, while retaining lightweight in-partition execution under low-contention conditions. The experimental results on both micro- and macro-benchmarks demonstrate that DCoS achieves a throughput up to three times higher than state-of-the-art CC protocols under high-contention workloads. |
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| Bibliographie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 2076-3417 2076-3417 |
| DOI: | 10.3390/app15116341 |