# Balloon: An Elastic Data Management Strategy for Interlaced Magnetic Recording
## 5 Performance Evaluation
### Persistent Buffer Cleanup Efficiency  
Of all the strategies evaluated, Balloon and MAGIC stand out by introducing a persistent buffer to reduce the frequency of track rewrites. However, frequent cleanups degrade disk performance [21; 22]. To further investigate the factors contributing to the performance gap between Balloon and MAGIC, this section focuses on evaluating the cleanup efficiency of their persistent buffers under varying space utilization and workloads. Therefore, the number of cleanups and the average size of the victims are used as performance metrics, which are commonly used in similar studies [26; 27; 28; 29].  
Figure 12a shows the buffer cleanup frequency of Balloon and MAGIC at different utilization and workloads. We can observe that both have lower cleanup frequencies at medium utilization than at high utilization. In particular, the cleanup frequency of Balloon is significantly lower than that of MAGIC in all scenarios. The reason for this is that Balloon allows only eligible data to enter the WIF-PB and swaps some buffer tracks from the top region directly to the bottom track, effectively increasing the efficiency of persistent buffer usage. In contrast, MAGIC indiscriminately directs write interference data to the persistent buffer, which results in frequent cleanups. Furthermore, Balloon takes proactive measures to retain hot data in the victims, preventing them from frequently moving in and out of the persistent buffer. This approach also contributes to a reduction in cleanup frequency. As another metric of cleanup efficiency, the average size of victims reflects the number of buffer blocks evicted per cleanup. The average size of the victims has a double meaning for evaluating the efficiency of each cleanup: first, the size of the victims somehow reflects the maximum spatial gain of the current cleanup operation; second, the size of the victims can reflect the effectiveness of Balloon in improving the spatial locality of the data. In general, the size of the victim is affected by a number of factors, including workload characteristics,  
Figure 11: The total number of extra I/Os introduced by Balloon and competitors under different space utilization and workloads.  
hot data identification mechanisms, and data management policies. Figure 12b shows the average size of victims evicted by Balloon and MAGIC for different utilization and workloads. Intuitively, the victim size of Balloon may not be as large as that of MAGIC, because it only redirects eligible data blocks to the persistent buffer. However, in most scenarios, the victim size of the Balloon is higher or close to that of MAGIC. Upon careful analysis, we find that most victims of the Balloon migrate from buffer tracks and tend to accumulate a significant number of data blocks before being swapped to the persistent buffer, potentially increasing the size of the victim. In a few cases, Balloon's victims are smaller than MAGIC's. The reason for the above result is that Balloon actively filters hot data from its victims, resulting in a reduction in victim size. In fact, Balloon reduces the cleaning frequency at the cost of reducing the size of the victims, which improves the efficiency of buffer cleaning in the long run. Therefore, the size of the victim is not a determining factor in the efficiency of the cleanup.