Because of the increasing speed gap between speed of compute and storage, caching is critical for improving the throughput of distributed file systems. It has been shown that prefetching can hide the latency resulted by network communication or disk operations. However, conventional client-based prefetching schemes are not efficient in distributed file systems as the limited computing and memory power of client nodes. In this paper, we present an effective and load-aware server-side prefetching scheme for distributed file systems, name SSPF. As an orthogonal approach, SSPF can be coupled with any existing caching scheme. First, SSPF exploits spatial locality to improve the efficiency of the prefetching cache and minimize memory requirement. Then, for maximizing the efficiency of cache, a multi-queue based cache manager is designed to coordinate between the prefetching blocks and other caching blocks. Furthermore, a heuristic-based request distribution strategy is proposed to optimize the balance between data server nodes and improve the overall performance. Finally, we have implemented and evaluated SSPF on the real distributed file system. Experimental results show that SSPF can significantly improve the read performance with negligible memory over-head.

Existing in-memory graph storage systems that rely on DRAM have scalability issues because of the limited capacity and volatile nature of DRAM. The emerging persistent memory (PMEM) offers us a chance to solve these issues through its larger capacity and non-volatile characteristics. However, simply adapting existing DRAM-based graph storage systems to PMEM would result in inefficient PMEM stores and accesses, including high read and write amplification to PMEM, imbalanced work division for PMEM accesses, and costly remote PMEM access across NUMA nodes. These issues severely limit the performance of large graph processing. In this article, we aim at achieving scalable and high-performance graph processing in PMEM. We first propose an XPLine-friendly graph storage model that uses vertex-centric graph buffering, hierarchical vertex buffer managing, and in-place vertex block merging to optimize PMEM graph storage. Furthermore, we develop a scalable graph processing model that leverages multi-threaded work dividing and NUMA-friendly graph accessing to optimize PMEM graph accesses. Based on these techniques, we implement XPGraph , a PMEM-based graph storage system for large-scale evolving graphs, and several variants for different system settings. Our experiments demonstrate that XPGraph surpasses the state-of-the-art in-memory graph storage system on a PMEM-based system by 3.07× to 4.99× in update performance and up to 5.87× in query performance, and performs much better in highly parallel multi-threaded scenarios.

Traditional in-memory graph storage systems have limited scalability due to the limited capacity and volatility of DRAM. Emerging persistent memory (PMEM), with large capacity and non-volatility, provides us an opportunity to realize the scalable and high-performance graph stores. However, directly moving existing DRAM-based graph storage systems to PMEM would cause serious PMEM access inefficiency issues, including high read and write amplification in PMEM and costly remote PMEM accesses across NUMA nodes, thus leading to the performance bottleneck. In this paper, we propose XPGraph, a PMEM-based graph storage system for managing large-scale evolving graphs, by developing an XPLine-friendly graph access model with vertex-centric graph buffering, hierarchical vertex buffer managing, and NUMA-friendly graph accessing. Experimental results show that XPGraph achieves 3.01× to 3.95× higher update performance and up to 4.46× higher query performance, compared with the state-of-the-art in-memory graph storage system implemented on a PMEM-based system.