Emerging memory interconnect (such as CXL and HCCS) promises rack-scale machine to become a reality, as the interconnect enables load/store accessible memory shared across the entire rack. However, the rack-scale shared memory poses two unique challenges on the operating system, primarily because of synchronization bottleneck and reliability issue. First, hardware cache coherence is not guaranteed, thus existing lock-based approach is ineffective to synchronize cross-node memory access. Second, memory faults significantly increase, and additional interconnect hops and switches expand fault surface and radius. As a result, current systems cannot efficiently leverage in-rack shared memory and instead manage rack resource in a disaggregated way, suffering from unnecessary networking/RDMA transmission overhead and redundant data copies.

The efficiency of metadata processing affects the file system performance significantly. There are two bottlenecks in metadata management in existing local file systems: 1) Path lookup is costly because it causes a lot of disk I/Os, which makes metadata operations inefficient. 2) Existing file systems have deep I/O stack in metadata management, resulting in additional processing overhead. To solve these two bottlenecks, we decoupled data and metadata management and proposed a metadata management layer for local file systems. First, we separated the metadata based on their locations in the namespace tree and aggregated the metadata into fixed-size metadata buckets (MDBs). This design fully utilizes the metadata locality and improves the efficiency of disk I/O in the path lookup. Second, we customized an efficient MDB storage system on the raw storage device. This design simplifies the file system I/O stack in the metadata management and allows metadata lookup to be completed with constant time complexity. Finally, this metadata management layer gives users the flexibility to choose metadata storage devices. We implemented a prototype called Otter. Our evaluation demonstrated that Otter outperforms native EXT4, XFS, Btrfs, BetrFS and TableFS in many metadata operations. For instance, Otter has 1.2 times to 9.6 times performance improvement over other tested file systems in file opening.