# ReZNS: Energy and Performance-Optimal Mapping Mechanism for ZNS SSD
## 1 Introduction  
With the rapid growth of cloud datacenters and their data volumes, energy efficiency has recently gained significant attention from researchers in academia and industry [1; 2; 3; 4; 5; 6; 7]. Specifically, as a result of the increasing evidence of the carbon emission problem on the environment and society, many cloud providers (e.g., AWS, Google, and Microsoft) and manufacturers (e.g., Samsung and Huawei) have invested substantial effort in improving energy efficiency [8; 9; 10; 11]. For example, Google announced a carbon-free energy plan by 2030, and Meta is focusing on renewable energy with wind and solar projects [12; 13]. Samsung Electronics is working to reduce the carbon emissions generated during the manufacturing process of TV, AV, display, and mobile devices [7]. Nevertheless, they still struggle with energy consumption because deep learning workloads (DL) requiring high-end resources (i.e., CPU, memory, and storage devices) are dominant and incur resource contention in multi-tenant environments [14; 15; 16; 17; 18]. Meanwhile, some researchers and engineers focused on storage stacks to isolate resource interference with other clients in cloud environments. They proposed a new type of storage device, called ZNS SSD, that efficiently guarantees the I/O performance of each running application by enabling the zoned namespace interface. The ZNS SSDs have gained popularity for proven high performance in diverse domains; they always show good performance by supporting all write requests in an append-only manner [19; 20; 21; 22; 23; 24; 25; 26]. For example, ZNS was proposed as a new mobile I/O stack adopting zoned abstraction to enhance performance with a low write amplification factor (WAF) in mobile devices [27]. Some researchers proposed a new interface and ZNS-aware filesystems for ZNS SSDs, and they showed the potential possibility for ZNS SSDs by implementing them in a real SSD device [20]. Several recent studies for ZNS SSDs were conducted based on SSD emulators, such as ConfZNS and NVMeVirt, which help to understand how ZNS works in detail [21; 28]. Unfortunately, clients in cloud environments may not use all the capacity allocated in each zone on ZNS SSD due to the behaviors of applications and frameworks. In this case, it wastes the internal capacity of ZNS SSD in that the capacity reserved in a zone cannot be used by other clients. Under-utilization in multiple zones on ZNS SSD can have a negative impact on overall energy consumption and performance because it frequently reclaims free space with zone-reset commands. Guided by these insights, we propose ReZNS, a new mapping mechanism inside ZNS SSD that monitors the unused space in reserved zones and dynamically allocates it to other clients. Therefore, ReZNS can provide high performance and energy efficiency as it achieves better space utilization with collaboration from other zones on ZNS SSD. We implement ReZNS in NVMeVirt and perform comprehensive experiments with diverse workloads, such as FIO, Filebench, and the Yahoo Cloud Serving Benchmark (YCSB) [29]. In particular, we compare the efficiency of ReZNS in terms of performance and energy consumption with baseline ZNS SSDs. Extensive experimental results clearly show that ReZNS improves overall performance by up to 60% and reduces the total energy consumption by up to 3% by efficiently decreasing the total number of zone-reset commands inside ZNS SSDs. We believe ReZNS is a good choice for future sustainable systems that mainly consider the overall performance and energy efficiency. In summary, the main contributions of this paper are:  
* We perform an in-depth breakdown to understand the internal behaviors of ZNS SSDs, including the mapping mechanism and zone-reset command. * We design and implement ReZNS, a novel mapping mechanism for ZNS SSDs that includes the management policy for the zones that are no longer in use and the mapping policy for sharing unused capacity.