Performance Optimization of Storage Engine Based on Non-Volatile Memory

Non-volatile memory has a read/write speed that is comparable to dynamic random access memory and can be used to replace traditional storage devices to improve the performance of storage engines. However, existing storage engines typically use generic block interfaces to access devices, resulting in a long I/O software stack, increasing read/write latency at software layers, thereby limiting the performance benefits of non-volatile memory. To solve this problem, this paper proposes a new storage engine, named NVMStore, which is based on non-volatile memory and the Ceph big-data storage system platform. NVMStore accesses storage devices through memory mapping and optimizes data read/write processes according to byte-addressability and data persistence characteristics of non-volatile memory, thus reducing the data write amplification and software stack overhead. Experimental results on real non-volatile memory devices show that NVMStore can significantly improve the performance of Ceph when dealing with small block data read/ write workloads, compared with traditional storage engines using non-volatile memory.