# Intelligent Transaction Scheduling to Enhance Concurrency in High-Contention Workloads
## 7 Experimental Results
### Microbenchmark Evaluation  
In this section, we evaluate the effectiveness of the DCoS DRL-based scheduler by comparing it against three state-of-the-art scheduling methods:  
* **Simulated Annealing (SA)**[10] explores scheduling configurations by probabilistically accepting higher-cost states.
* **Dynamic Priority (Priority)**[33] prioritizes transactions based on the largest number of pending operations.
* **Greedy Scheduling (Greedy)** selects immediately available thread workers using a first-fit allocation strategy.  
We design a workload generator to evaluate the performance of DCoS under high-contention with varying workload skew. We sample a set of hot data items from the database and then test a set of transactions \(|\mathcal{D}|\) that will operate on these hot data items. With the increase of concurrent contention-intensive transactions, the contention rate also increases. We manage workload variance by adjusting the number of cold items, while the processing time for hot transactions, denoted as \(t_{p}\), can vary with the cold item distribution. Specifically, we define three workload states among cores based on average transaction-processing time: \(t_{p}\sim\mathcal{U}(100,120)\upmu\)s for low workload variance, \(t_{p}\sim\mathcal{U}(100,150)\upmu\)s for medium workload variance, and \(t_{p}\sim\mathcal{U}(100,200)\upmu\)s for high workload variance.  
**Varying contention levels.** We set the available thread workers to 5 and the number of concurrent contention-intensive transactions (10, 15, 20) to simulate increasing contention rates. The DCoS model was trained for 1000 episodes until convergence was reached, ensuring stable policy learning. We generated 500 distinct transaction workload sets not encountered during training for testing. The mean value of the results is used for comparison; we collect the makespan of processing the batch of hot items as shown in Figure 8a. DCoS consistently outperforms other algorithms across all scenarios, particularly under high workload variance and large concurrent transactions. Compared to the best-performing SA, DCoS demonstrates a makespan reduction of 8.8% to 12.8% under low workload variance and 14% to 15.5% under high workload variance as the number of concurrent transactions increases. The Greedy algorithm shows performance decreases in handling increased concurrency, which aligns with the inherent weakness of greedy approaches, which make locally optimal but globally suboptimal decisions. The priority algorithm performs comparably to SA under moderate conditions; however, with workload variance increases, the performance among SA, Greedy, and Priority is affected due to their lack of adaptability to dynamic adjustments, which limits their effectiveness.  
**Generalization.** To evaluate cross-scale generalization, we conduct transfer learning experiments where DCoS trained on small-scale configurations (five thread workers, 20 concurrent contention-intensive transactions) is directly deployed in large-scale environments (ten thread workers, 20-30 concurrent high-contention transactions). This zero-shot adaptation test eliminates parameter fine-tuning to isolate model generalization capacity. The evaluation protocol maintains 10 worker threads while scaling contention-intensive transactions to represent 100-150% of the original training workloads. Figure 8b shows the testing results, we observe a similar trend to small-scale transactions, with DCoS demonstrating a 12.57% makespan reduction compared to the best-performing SA. Since we implement embedding layers to enable DCoS to generalize effectively to different sizes of concurrent transaction sets and thread workers, even without additional training, this allows DCoS to adapt to various transaction workload scenarios.  
Figure 8: \(Cont\).