# Amphishea: A Novel Persistent Buffer Management Strategy to Improve SMR Disk Performance
## 6 Experiment
### Performance Evaluation  
In this section, we evaluate the effect of Amphisheaena with chunk = 64 KB and \(\lambda\) = 0.1, and other state-of-the-art persistent buffer management strategies on improving disk performance. Average response latency, write buffer hit rates, number of RMW and RMW-free, write amplification, padded data volume, and average write-back victim zone size are used to comprehensively evaluate the efficiency of Amphisheaena. The X-axis of each graph represents different workloads and the Y-axis represents specific metrics. **Normalized average response latency. ** We first compare the average I/O response latency of Amphishea with that of the adversary to evaluate the overall performance improvement of the disk. The response latency under all traces is normalized to Amphisheaena, and the experimental results are shown in Figure 12. It can be observed that Amphisheaa reduces the response latency by a maximum of 82.4% and a minimum of 4.8% compared to Fluid_loop (i.e., in traces src2_2 and src2_0). These two traces are the spatial locality-dominated and the temporal locality-dominated workloads, respectively. One of the reasons Amphishea can achieve I/O performance improvements is that the space allocator uses a custom buffer management unit to manage each buffer, effectively balancing the cleanup and writing efficiency. The ability to dynamically adjust the size of each \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Trace Name** & **Total Reqs** & **Write Ratio** & **Update Ratio** & **Write Range** & **Avg. Write Len** \\  & **(Million)** & **(\%)** & **(\%)** & **(Zone)** & **(Sector)** \\ \hline hm\_0 & 3.99 & 64.5 & 92.7 & 56 & 18.4 \\ prrn\_0 & 5.58 & 89.2 & 73.7 & 265 & 22.4 \\ prxy\_0 & 12.51 & 96.9 & 98.7 & 83 & 14.4 \\ ts\_0 & 1.80 & 82.4 & 95.6 & 88 & 16.8 \\ web\_0 & 2.02 & 70.1 & 93.9 & 136 & 17.6 \\ src2\_2 & 1.15 & 69.7 & 23.7 & 678 & 102.4 \\ src2\_0 & 1.55 & 88.7 & 83.3 & 63 & 15.2 \\ usr\_0 & 2.23 & 60 & 91.5 & 64 & 20.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Characteristics of evaluated workloads. buffer as the workload changes is another reason. In addition, the latency-sensitive garbage collection policy is also beneficial in reducing the time user requests that are blocked. **Write hit rates. ** Write hit rates represent the ratio of the number of buffered blocks updated to the total number of blocks written. The higher the write hit rate, the less frequently the buffer is cleaned, which contributes to stable SMR disk I/O performance. It is important to note that Amphisheana's overall hit rate consists of two parts. As shown in Figure 13, the HFB hit rate is represented by the black bar at the bottom, and the LFB hit rate is represented by the white bar at the top. Compared to Fluid and Skylight, Fluid_loop re-queues hot blocks in victims to the head of the log queue to avoid premature eviction, and thus its write-hit rate outperforms both in most traces. Benefiting from the efficiency of the two-phase block classification scheme, the write-hit rate of Amphisheana is higher than that of its competitors in all traces. In particular, in src2_2, where spatial localization dominates, the write-hit rate of Amphisheana is at least 5.8% higher than that of Skylight which achieves the suboptimal result. **Normalized write amplification. ** As shown in Figure 14, Amphisheana exhibits the lowest write amplification under all workloads, with an average reduction of 98.5% compared to other strategies. The reason behind this is that Amphisheana utilizes fragmentation-level metrics to reduce the formation of fragmented data layouts, thereby reducing redundant RMW operations and achieving lower write amplification. While Skylight requires each CMR zone to hold data from multiple SMR zones, this triggers multiple RMW operations during the cleanup process, increasing the likelihood of write amplification.