Intelligent Transaction Scheduling to Enhance Concurrency in High-Contention Workloads
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| Titel: | Intelligent Transaction Scheduling to Enhance Concurrency in High-Contention Workloads |
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| Autoren: | Shuhan Chen, Congqi Shen, Chunming Wu |
| Quelle: | Applied Sciences, Vol 15, Iss 11, p 6341 (2025) |
| Verlagsinformationen: | MDPI AG, 2025. |
| Publikationsjahr: | 2025 |
| Bestand: | LCC:Technology LCC:Engineering (General). Civil engineering (General) LCC:Biology (General) LCC:Physics LCC:Chemistry |
| Schlagwörter: | in-memory database, transaction decomposition, transaction dependency, deep reinforcement learning, high-contention workloads, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999 |
| Beschreibung: | 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. |
| Publikationsart: | article |
| Dateibeschreibung: | electronic resource |
| Sprache: | English |
| ISSN: | 2076-3417 |
| Relation: | https://www.mdpi.com/2076-3417/15/11/6341; https://doaj.org/toc/2076-3417 |
| DOI: | 10.3390/app15116341 |
| Zugangs-URL: | https://doaj.org/article/bc888511a1f24939b727956fdaac9596 |
| Dokumentencode: | edsdoj.bc888511a1f24939b727956fdaac9596 |
| Datenbank: | Directory of Open Access Journals |
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Nájsť tento článok vo Web of Science | Abstract: | 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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| ISSN: | 20763417 |
| DOI: | 10.3390/app15116341 |