Object storage is increasingly attractive for deep learning (DL) applications due to its cost-effectiveness and high scalability. However, it exacerbates CPU burdens in DL clusters due to intensive object storage processing and multiple data movements. Data processing unit (DPU) offloading is a promising solution, but naively offloading the existing object storage client leads to severe performance degradation. Besides, only offloading the object storage client still involves redundant data movements, as data must first transfer from the DPU to the host and then from the host to the GPU, which continues to consume valuable host resources.

Emerging cloud-native distributed databases rely on local NVMe SSDs to provide high-performance and highavailable data services to many cloud applications. However, the database clusters suffer from low utilization of local storage because of the imbalance between CPU and storage capacities within each node. For instance, the OceanBase distributed database cluster, with hundreds of PB local storage capacity, only utilizes around 40% of its local storage. Although disaggregated storage (EBS) can enhance storage utilization by provisioning the CPU and storage independently on demand, they suffer from performance bottlenecks and high costs. In this paper, we propose LightPool, a high-performance and lightweight storage pool architecture large-scale deployed in the OceanBase clusters, enhancing storage resource utilization. The key idea of LightPool is aggregating cluster storage into a storage pool and enabling unified management. In particular, LightPool adopts NVMe-oF to enable high-performance storage resource sharing among cluster nodes and integrate the storage pool with Kubernetes to achieve flexible management and allocation of storage resources. Furthermore, we design the hot-upgrade and hot-migration mechanisms to enhance the availability of LightPool. We have deployed LightPool on over 8500 nodes in production clusters. Statistics show that LightPool can improve storage resource utilization from about 40% to 65%. Experimental results show that the extra latency from LightPool is only about 2.1 μs compared to local storage. Compared to OpenEBS, LightPool enhances bandwidth up to 190.9% in microbenchmarks and throughput up to 6.9% in real-world applications. LightPool is the best practice to deploy NVMe-oF (NVMe/TCP) in the production environment. We also discuss important lessons and experiences learned from the development of LightPool.

NVMe over Fabrics (NVMe-oF) has been widely applied as a remote storage protocol in cloud computing. The existing NVMe-oF software stack consumes a large number of CPU resources. Emerging devices, such as Smart NICs and DPUs, have supported hardware offloading of NVMe-oF to free these valuable CPU cores. However, NVMe-oF offloading capacity is always compromised because of limited hardware resources on design. Additionally, from thorough evaluations, we found that NVMe-oF inevitably suffers from severe performance degradation on complex application I/O patterns when using hardware offloading. It is challenging to achieve high performance and fully utilize NVMe-oF offloading simultaneously. In this paper, we propose HyQ, a novel hybrid I/O queue architecture for NVMe-oF, to achieve high performance while gaining the advantages of hardware offloading. HyQ realizes the coexistence of hardware offloading and software non-offloading queues, thus enabling the dynamic dispatching of I/O requests to appropriate processing queues according to user-defined I/O scheduling policies. Additionally, HyQ provides a request scheduling framework to support customized schedulers that select appropriate queues for I/O requests. In our evaluation, HyQ achieves up to 1.91x IOPS and 8.36x bandwidth performance improvement over the original hardware offloading scheme.