# Balloon: An Elastic Data Management Strategy for Interlaced Magnetic Recording
## 1 Introduction
To take advantage of this property, data shuffling policies periodically or actively swap hot (frequently updated) data from the bottom track with cold data from the top track, at either track-level or block-level granularity. For example, [PERSON] et al. [15] proposed three track-level data shuffling policies that minimize rewrite overhead by periodically moving bottom hot tracks to a persistent buffer or top tracks. Similarly, MOM [11] reduces extra I/Os by actively moving data to be rewritten to the free top track instead of backing up and restoring the data. Recently, [PERSON] et al. [16] introduced two block-level data shuffling policies to improve the efficiency of data migration. The block-level data shuffling policy more accurately identifies hot data blocks, thereby improving the efficiency of each swap. In fact, data placement and data shuffling reduce the frequency of track rewrites from different perspectives; it is practical to use them together to achieve greater performance gains [10; 14; 16]. Nevertheless, existing research lacks a comprehensive solution for efficiently combining data placement and data shuffling. In particular, there is  
Figure 1: Track layout comparison for CMR, SMR, and IMR. a non-negligible performance gap between IMR disks and CMR disks under high space utilization, which greatly affects the practical use of IMR disks. To mitigate the performance degradation caused by track rewrites, this paper presents an elastic data management strategy called Balloon, which is a device-level solution similar to the flash translation layer and the shingled translation layer. The principle behind this strategy is to proactively allocate incoming write interference data to the free top track. This not only eliminates the rewrite overhead of direct read-modify-write (RMW) operations but also enables low-cost data shuffling using the buffered data in the top track at no additional cost. As a result, the strategy uses on-demand data shuffling to relocate hot and cold data to specific regions, minimizing the frequency of track rewrites and the impact of data migration on user requests. The contributions of this research can be summarized as follows:  
* An adaptive write interference data placement policy is proposed that reduces unnecessary rewrite overhead by judiciously placing data in tracks with low rewrite probability and opportunistically using the free top track to buffer write interference data. Meanwhile, a lightweight Bloom-filter-based hotness scoring mechanism for tracks is established to better balance the accuracy and memory overhead of track-level hotness identification. * An on-demand data shuffling policy is introduced to further improve data placement efficiency. During data shuffling, hot tracks with low update block coverage are relocated to reserved regions of the disk to minimize the frequency of track rewrites, while tracks with high update block coverage are placed in the top region promptly to reduce seek time for subsequent update requests. * To boost the collaborative efficiency of data placement and data shuffling, a write-interference-free persistent buffer is introduced. The persistent buffer dynamically adjusts admission constraints based on space utilization, thereby reducing the cleanup frequency. Moreover, it selectively evicts data blocks to minimize the frequency of track rewrites. * The results of our evaluation show that the proposed strategy improves IMR write performance by up to 68.7% and 78.8% on average at medium and high space utilization, respectively, compared with state-of-the-art studies. The rest of this paper is organized as follows. Section 2 describes the background and related work. The motivation is then presented in Section 3. Details of the Balloon design and implementation instructions are given in Section 4. Evaluation results and analysis are given in Section 5. Finally, Section 6 summarizes the study.