# Data Placement Using a Classifier for SLC/QLC Hybrid SSDs
## 4 Performance Evaluation
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline Utilization (\%) & 0–20 & 20–30 & 30–40 & 40–50 & 50–60 & 60–70 & 70–100 \\ \hline SLC cache (\%) & 56 & 50 & 40 & 30 & 25 & 20 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Dynamic allocation of the SLC cache space. the need for frequent data migration and garbage collection operations, which are both time-consuming and resource-intensive. In particular, we observed that the migration time was significantly reduced; there was an improvement of 76% and 67% compared to the No-classifier and Size-based schemes, respectively. Consequently, this efficient machine-learning-based data management leads to a reduction in overall execution time, enhancing the performance of the SSD. In Figure 4, we present the experimental results with a focus on the erase count, a critical metric directly affecting the longevity and durability of SSDs. In our analysis, we specifically measured the erase count within the QLC region. The results of this experiment further reinforce the effectiveness of our proposed data placement method. When the SLC cache size is set to represent 5% of the total SSD capacity, our method significantly outperforms the other approaches. It achieves an impressive reduction in erase counts by 85%, 81%, and 80% compared to the QLC-only, No-classifier, and Size-based schemes, respectively. This substantial decrease in erase counts is a clear indicator of the enhanced efficiency and lifespan of SSDs using our method. However, it is noteworthy that when the SLC cache size is increased to 20% of the SSD's total capacity, there is a noticeable increase in erase counts across all schemes, including ours. This increase can be primarily attributed to the consequent reduction in the size of the QLC region. A smaller QLC region leads to more frequent garbage collections within this area, as the available space for storing new data diminishes more rapidly. This observed trend particularly highlights a significant limitation of the Size-based method. It reveals that using a fixed threshold for distinguishing between hot and cold data, as done in the Size-based scheme, can lead to suboptimal performance. This approach lacks the flexibility to adapt to changes in workload patterns and variations in the SSD's storage status. Consequently, while it might offer some benefits in specific scenarios, its overall effectiveness in enhancing the SSD's lifespan can be limited compared to more dynamic and adaptive methods like ours. This insight underscores the importance of employing a data placement strategy that is not only efficient but also adaptable to the changing conditions and demands of SSD usage. The efficacy of our proposed data placement method, particularly its sensitivity to varying workload characteristics, necessitates thorough validation to gauge its adaptability to diverse I/O patterns. This is crucial to ensure that the method remains effective across a wide range of real-world scenarios. In Table 4, we present experimental results that shed light on this crucial aspect of our approach. When the method is trained and tested on the same workload, it achieves an accuracy rate exceeding 90%. Conversely, testing with the VDI workload after training with the YCSB workload results in a 77.5% accuracy rate, and the reverse scenario yields a 61% accuracy rate. Although accuracy diminishes when training and testing are conducted with different workloads, our method's overall performance remains robust and significant. This minor reduction in accuracy is mainly  
Figure 4: Erase count as a function of SLC cache size. observed during periods of rapidly changing I/O workloads, wherein the predictive model might momentarily struggle to keep up with evolving data access patterns. It is important to note that this minor decrease in accuracy is predominantly a transient phenomenon.