Disaggregated memory separates compute and memory resources into independent pools connected by fast RDMA (Remote Direct Memory Access) networks, which can improve memory utilization, reduce cost, and enable elastic scaling of compute and memory resources. Hash indexes provide high-performance single-point operations and are widely used in distributed systems and databases. However, under disaggregated memory, existing hash indexes suffer from write performance degradation due to high resize overhead and concurrency control overhead. Traditional write-optimized hash indexes are not efficient for disaggregated memory and sacrifice read performance. In this paper, we propose SepHash, a write-optimized hash index for disaggregated memory. First, SepHash proposes a two-level separate segment structure that significantly reduces the bandwidth consumption of resize operations. Second, SepHash employs a low-latency concurrency control strategy to eliminate unnecessary mutual exclusion and check overhead during insert operations. Finally, SepHash designs an efficient cache and filter to accelerate read operations. The evaluation results show that, compared to state-of-the-art distributed hash indexes, SepHash achieves a 3.3X higher write performance while maintaining comparable read performance.

Previous works proposed building file systems and organizing the metadata with KV stores because KV stores handle entries of various sizes efficiently and have excellent scalability. The emergence of the byte-addressable persistent memory (PM) enables metadata service to be faster than before by tailoring the KV store for the PM. However, existing PM-based KV stores cannot handle the workloads of file systems’ metadata well because simply depending on hash tables or trees cannot simultaneously provide fast file accessing and efficient directory traversing. In this paper, we exploit the insight of the metadata operations and propose the PetaKV, a KV store tailored for the metadata management of file systems on PM. PetaKV leverages dual hash indexing to achieve fast file put and get operations. Moreover, it cooperates with PM-tailored peta logs to collocate KV entries for each directory, thus supporting efficient directory scans. Our evaluation indicates PetaKV outperforms state-of-art tree-based KV stores on put, get and scan$2.5\times$,$3.2\times$, and$2.8\times$on average, respectively. Moreover, the file system built with PetaKV achieves$1.2\times$to$6.4\times$speedup compared to those built with tree-based KV stores on the metadata operations.