# Leveraging Static and Dynamic Wear Leveling to Prolong the Lifespan of Solid-State Drives
## 4 Performance Evaluation
### Evaluation of Greedy-MP  
The performance of the Greedy-MP policy, a dynamic wear leveling policy designed with consideration for static wear leveling, is compared with the PWL, CB+PWL, and WOGC+PWL policies. Figure 6 compares the SSD lifespan achieved by each policy across different traces. The X-axis in each figure represents the proportion of storage space that was initialized with sequential and random writes before the measurement began, as explained in Section 4.2. The Y-axis shows the cumulative number of sectors processed by the host's write requests until the SSD reached the end of its lifespan. As in the experiment described in Section 4.2, we considered the SSD to have reached the end of its lifespan when overprovisioning fell below 10% due to the occurrence of bad blocks. For each initialization ratio on the X-axis, after performing one sequential write initialization and three random write initializations, each trace was repeatedly applied as input to the simulator until the SSD reached the end of its lifespan. The results demonstrate that the Greedy-MP policy is highly effective in extending the lifespan of SSDs. For all traces and the majority of initialization ratios, the Greedy-MP policy exhibits the longest SSD lifespan. Namely, it processed the most host write requests until the SSD reached the end of its lifespan. Compared to the PWL policy, which only applies static wear leveling, the Greedy-MP policy extended the lifespan by up to 1.07-1.72 times, depending on the trace. Additionally, compared to CB+PWL, the Greedy-MP policy extended the lifespan by up to 1.17-1.99 times. Furthermore, it extended the lifespan by up to 1.27-3.56 times compared to the WOGC+PWL policy. This indicates that the policy, which delays the occurrence of bad blocks by separately managing blocks that have been excessively erased and migrated, is effective in delaying bad block occurrence. Meanwhile, in most policies, as the initialization ratio increases, the SSD lifespan initially increases but then begins to decrease. If the initialization ratio is too low, the proportion of cold data is also very low, making it difficult to effectively utilize the PWL policy, which consolidates cold data into worn-out blocks. Conversely, if the initialization ratio is too high, a significant portion of the SSD is filled with valid data, leading to higher valid page ratios of victim blocks during garbage collection. This reduces the number of clean pages reclaimed in each garbage collection process, resulting in more frequent garbage collections. Consequently, the erase count of blocks increases at a faster rate, ultimately reducing the SSD's lifespan. Figure 7 displays the average number of block erasures across all blocks at the point when the SSD lifespan terminates due to the occurrence of bad blocks. The results show that the Greedy-MP policy achieves an average block erase count close to 100 for all traces and the majority of initialization ratios. Considering that the maximum erase count per block is set to 100, it can be observed that the Greedy-MP policy maintains the block erase count almost evenly. As a result, as shown in Figure 6, it significantly extends the lifespan of the SSD compared to other policies. Among the six traces used in the experiment, cam01 and weba exhibit relatively read-intensive characteristics, while the remaining traces are write-intensive (as detailed in Section 4.1 and Table 2). The results, shown in Figures 6 and 7, indicate that Greedy-MP consistently achieves near-maximum block erase counts across all traces and initialization ratios, leading to a longer SSD lifespan compared to other policies. In contrast, CB+PWL and WOGC+PWL policies exhibit very low average block erase counts under low initialization ratios, particularly in scenarios where writes occurred within a relatively small logical address space, including the read-intensive traces. This indicates ineffective wear leveling when the ratio of cold data is low.