PolarFS is a distributed file system with ultra-low latency and high availability, designed for the POLARDB database service, which is now available on the Alibaba Cloud. PolarFS utilizes a lightweight network stack and I/O stack in user-space, taking full advantage of the emerging techniques like RDMA, NVMe, and SPDK. In this way, the end-to-end latency of PolarFS has been reduced drastically and our experiments show that the write latency of PolarFS is quite close to that of local file system on SSD. To keep replica consistency while maximizing I/O throughput for PolarFS, we develop ParallelRaft , a consensus protocol derived from Raft, which breaks Raft's strict serialization by exploiting the out-of-order I/O completion tolerance capability of databases. ParallelRaft inherits the understand-ability and easy implementation of Raft while providing much better I/O scalability for PolarFS. We also describe the shared storage architecture of PolarFS, which gives a strong support for POLARDB.

Natural image matting plays an important role in image and video editing. It has been addressed hotly because it is inherently an under-constrained problem - we must estimate both the foreground and background colors (we call them component colors) at each pixel to calculate its opacity, according to the only known observed color. Prior assumption such as statistics and smoothness are utilized for estimation, but these methods either are simple to handle situations such as complex background and large interaction region or have high computational complexity. This paper proposes a fast technique to estimate the component colors based on structure information. Our approach exploits a simple convolution operation to detect structure information in images. It then uses two kinds of estimation methods to propagate colors based on the structure types. Experimental results show that our method is fast and efficient to handle objects with strong structures and large part of interaction region with the background.