# Balloon: An Elastic Data Management Strategy for Interlaced Magnetic Recording
## 1 Introduction  
With the advent of new technologies such as cloud storage, scientific computing, medical imaging, and social networking, the creation of digital content is exploding, driving the need for cost-effective, high-capacity storage in the modern data center [1, 2, 3]. Conventional Magnetic Recording (CMR) disks, known for their affordability and reliability, have been fundamental in constructing modern storage systems. However, their potential to achieve higher areal density gains through reducing bit size has seemingly reached its limits due to the superparamagnetic effect (SPE) [4]. In response to this challenge, various magnetic recording technologies that utilize energy-assisted and optimized track layouts have been proposed to overcome the existing dilemma [5, 6, 7, 8]. Among these candidates, Shingled Magnetic Recording (SMR) and Interlaced Magnetic Recording (IMR) technologies are considered to be cost-effective and have therefore gained widespread attention in recent years [9]. CMR disk (Figure 1a) tracks are separated by guard spaces, allowing random writes without affecting adjacent tracks. In contrast, the SMR disk (Figure 1b) reduces track pitch by enlarging the write heads and subtly overlapping the tracks like shingles to achieve higher areal densities. However, this gain in capacity comes at the cost of introducing a significant amount of extra I/Os, as random writes to any track require time-consuming cascading track rewrites to maintain the integrity ofadjacent valid data. Another promising successor to CMR, IMR, uses both heat-assist technology and an interlaced track layout to achieve capacity gains [8]. As shown in Figure 1c, an IMR disk conceptually divides tracks into the top and bottom tracks. Each narrower top track (smaller track pitch) is interleaved with two wider bottom tracks (higher line density). Writing to the bottom track requires stronger laser power than the top track, which introduces write interference that corrupts valid data in the top track. Consequently, writing to the bottom track requires rewriting two adjacent top tracks, a process known as read-modify-write (RMW). Although the write penalties of IMR are obviously lower than those of SMR, frequent RMWs degrade disk performance as space utilization (allocated track ratio) increases, limiting their use at scale [10; 11]. To date, there have been several efforts from academia and industry to reduce the frequency of track rewrites. These efforts mainly focus on optimizing data placement and data shuffling techniques. Data placement policies that take into account the write interference effect, statically or dynamically allocate tracks for incoming data to minimize unnecessary rewrite overhead. For example, a typical three-phase write management method [12; 13] places incoming new data evenly across free tracks based on space utilization to delay the onset of RMW as much as possible. On this basis, [PERSON] et al. [14] proposed a zigzag track allocation policy to improve the spatial locality of logical data blocks, resulting in reduced seek time. Compared with the static data placement policy, the dynamic data placement policy further reduces the track rewrite frequency by considering the track hotness. For example, during the top track allocation phase, MAGIC [10] attempts to place data on the free top track adjacent to the cold (infrequently updated) bottom track and redirects write interference data (if there is valid data on the top tracks adjacent to the bottom tracks, writing to these bottom tracks will cause write interference. Therefore, the term \"write interference data\" is used to describe data written to the bottom tracks that cause such interference) to a persistent buffer inside the disk, effectively reducing the introduction of track rewrite overhead. In practice, the top track can be written freely without introducing rewrite overhead.