# Leveraging Static and Dynamic Wear Leveling to Prolong the Lifespan of Solid-State Drives
## 4 Performance Evaluation
### Static vs. Dynamic Wear Leveling  
Before evaluating the effects of the Greedy-MP policy, we assessed the lifespan of SSDs for policies performing dynamic wear leveling alone, policies performing static wear leveling alone, and policies performing both existing dynamic and static wear leveling. The SSD was considered to have reached the end of its lifespan when overprovisioning fell below 10% due to the occurrence of bad blocks, at which point we compared the number of sectors processed by host write requests. To assess the impact of cold data--rarely modified after writing--on each policy's effectiveness, we first performed a sequential write initialization followed by three random write initializations on a portion (0% to 50%) of the SSD's total exported capacity of 51.2 GB. For instance, during a 50% initialization, approximately 25.6 GB was sequentially written in 64-page units, starting from the first sector. Subsequently, three rounds of \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Time Stamp** & **Host Name** & **Disk Number** & **Type** & **Offset** & **Size** & **Response Time** \\ \hline
128166372002993000 & usr & 0 & Read & 10995532800 & 16384 & 30123 \\
128166372010284000 & usr & 0 & Write & 3207667712 & 24576 & 82327 \\... &... &... &... &... &... &... \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Trace Format. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Trace** & **Entire Logical Space** & **Read Logical Space (GB)** & **Written Logical Space (GB)** & **R/W Ratio (\%)** & **Read/Written Bytes (GB)** \\ \hline CAM-01-SRV-lvm0(cam01) & 15.9 & 2.1 & 0.7 & 73/27 & 35.3/13.1 \\ hm\_0(hm0) & 13.9 & 2.0 & 1.7 & 33/67 & 10.0/20.5 \\ prn\_0(pm0) & 41.3 & 3.8 & 12.4 & 22/78 & 13.1/46.0 \\ proj\_0(proj0) & 16.2 & 3.2 & 1.8 & 6/94 & 9.0/144.3 \\ CAMRESSTGA01-lvm0(stga) & 10.8 & 6.2 & 0.4 & 33/67 & 7.3/15.1 \\ CAMRESWEBA03-lvm0(weba) & 33.9 & 7.7 & 0.7 & 60/40 & 17.4/11.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Trace Attributes. random write initialization were conducted on the same area. During each random write initialization, random writes were repeated until 25.6 GB, representing 50% of the SSD's exported capacity, had been written. The target sector for each random write request was determined randomly, and the size of each write request was also randomly selected to range from 1 to 64 pages. Thus, a request could write as few as 1 page or as many as 64 pages at once. As shown in Table 2, because the address space accessed by write requests in most traces is very small compared to the SSD's exported capacity, the data written during the initialization process remains as cold data, with little to no modification afterward. After completing the initialization process, each trace was used as input data to measure the SSD's lifespan. Since the traces used in the experiments represent I/O data over a one-week period, running each trace only once does not result in a significant number of bad blocks on the SSD. Therefore, we repeatedly applied each trace as input until the SSD reached the end of its lifespan. Figure 5 shows the lifespan of SSDs achieved by each policy in each trace. The relative lifespan is displayed on the Y-axis, relative to the values when no static or dynamic wear leveling is performed. So, a result of 1 means the lifespan is the same as when no wear leveling is done. The X-axis represents the proportion of the storage capacity initialized with sequential and random writes before the experiment started. In the figure, CB and WOGC represent the results obtained when only dynamic wear leveling is performed. CB+PWL and WOGC+PWL, on the other hand, depict the outcomes when the PWL policy is implemented alongside dynamic wear leveling. PWL stands for the results achieved with static wear leveling alone. The results indicate the following facts:  
* The effectiveness of wear leveling increases with a higher proportion of cold data. This trend is observed across all policies.