# A Cache Policy Based on Request Association Analysis for Reliable NAND-Based Storage Systems
## 5 Performance Evaluation
### Experimental Results: Improvement of the Average Hit Ratio  
For MSR-usr_0, Figure 10a shows that RAA makes 42.25%, 28.77% and 35.27% improvements of the average hit ratio when compared with LRU, ARC, and VBBMS, respectively. Furthermore, RAA-Delay makes 28.76%, 17.09% and 22.75% improvements of the average hit ratio when compared with LRU, ARC, and VBBMS, respectively. For Systor'17-LUN_0, Figure 10b shows that RAA makes 60.45%, 27.68% and 40.94% improvements of the average hit ratio when compared with LRU, ARC, and VBBMS, respectively. Furthermore, RAA-Delay makes 55.80%, 24.29% and 37.04% improvements of the average hit ratio when compared with LRU, ARC, and VBBMS, respectively. For Systor'17-LUN_1, Figure 10c shows that RAA makes 39.99%, 17.99% and 27.85% improvements of the average hit ratio when compared with LRU, ARC, and VBBMS, respectively. Furthermore, RAA-Delay makes 34.60%, 13.62% and 23.01% improvements of the average hit ratio when compared with LRU, ARC, and VBBMS, respectively. For Systor'17-LUN_2, Figure 10d shows that RAA makes 29.46%, 21.65% and 24.20% improvements of the average hit ratio when compared with LRU, ARC, and VBBMS, respectively. Furthermore, RAA-Delay makes 27.98%, 20.29% and 22.81% improvements of the average hit ratio when compared with LRU, ARC, and VBBMS, respectively.  
For Systor'17-LUN_3, Figure 10e shows that RAA makes 43.07%, 18.52% and 29.26% improvements of the average hit ratio when compared with LRU, ARC, and VBBMS, respectively. Furthermore, RAA-Delay makes 33.75%, 10.89% and 20.88% improvements of the average hit ratio when compared with LRU, ARC, and VBBMS, respectively. For Systor'17-LUN_4, Figure 10f shows that RAA makes 28.68%, 14.73% and 21.39% improvements of the average hit ratio when compared with LRU, ARC, and VBBMS, respectively. Furthermore, RAA-Delay makes 24.38%, 11.05% and 17.41% improvements of the average hit ratio when compared with LRU, ARC, and VBBMS, respectively. For Systor'17-LUN_6, Figure 10g shows that RAA makes 33.18%, 16.15% and 24.82% improvements of the average hit ratio when compared with LRU, ARC, and VBBMS, respectively. Furthermore, RAA-Delay makes 30.16%, 13.59% and 22.01% improvements of the average hit ratio when compared with LRU, ARC, and VBBMS, respectively. For Financial, Figure 10h shows that RAA makes 3.58%, 2% and 4.6% improvements of the average hit ratio when compared with LRU, ARC, and VBBMS, respectively. Furthermore, RAA-Delay makes 3.48%, 1.91% and 4.5% improvements of the average hit ratio when compared with LRU, ARC, and VBBMS, respectively.  
We summarize the improvement of the average hit ratio in Table 3. For MSR-usr_0, Systor'17-LUN_0, Systor'17-LUN_1, Systor'17-LUN_2, Systor'17-LUN_3, Systor'17-LUN_4 and Systor'17-LUN_6, RAA and RAA-Delay can significantly improve the average hit ratio in Table 3.  
hit ratio under different cache sizes (i.e., 4, 8, 16, 32 and 64 MB), when compared with LRU, ARC, and VBBMS. This is because the seven workload traces contain more certain associations between the accessed data so that RAA and RAA-Delay can find the association rules and keep them as the strong request data in the cache as much as possible. For Financial, RAA and RAA-Delay only slightly improve the average hit ratio under different cache sizes (i.e., 4, 8, 16, 32 and 64 MB), when compared with LRU, ARC, and VBBMS. This is because Financial could contain more random accesses and less certain associations between the accessed data so that RAA and RAA-Delay are a little better than LRU, ARC, and VBBMS. As a result, we must point out that RAA and RAA-Delay consider the temporal/spatial locality and the association rules together so that they can improve the cache hit ratio when the workload traces tend to certain associations between the accessed data.