Byte-addressable non-volatile memory (NVM), as a new participant in the storage hierarchy, gives extremely high performance in storage, which forces changes to be made on current filesystem designs. Page cache, once a significant mechanism filling the performance gap between Dynamic Random Access Memory (DRAM) and block devices, is now a liability that heavily hinders the writing performance of NVM filesystems. Therefore state-of-the-art NVM filesystems leverage the direct access (DAX) technology to bypass the page cache entirely. However, the DRAM still provides higher bandwidth than NVM, which prevents skewed read workloads from benefiting from a higher bandwidth of the DRAM and leads to sub-optimal performance for the system. In this paper, we propose RCache, a read-intensive workload-aware page cache for NVM filesystems. Different from traditional caching mechanisms where all reads go through DRAM, RCache uses a tiered page cache design, including assigning DRAM and NVM to hot and cold data separately, and reading data from both sides. To avoid copying data to DRAM in a critical path, RCache migrates data from NVM to DRAM in a background thread. Additionally, RCache manages data in DRAM in a lock-free manner for better latency and scalability. Evaluations on Intel Optane Data Center (DC) Persistent Memory Modules show that, compared with NOVA, RCache achieves 3 times higher bandwidth for read-intensive workloads and introduces little performance loss for write operations.

Traditional named entity recognition methods need professional domain knowledge and a large amount of human participation to extract features, as well as the Chinese named entity recognition method based on a neural network model, which brings the problem that vector representation is too singular in the process of character vector representation. To solve the above problem, we propose a Chinese named entity recognition method based on the BERT-BiLSTM-ATT-CRF model. Firstly, we use the bidirectional encoder representations from transformers (BERT) pre-training language model to obtain the semantic vector of the word according to the context information of the word; Secondly, the word vectors trained by BERT are input into the bidirectional long-term and short-term memory network embedded with attention mechanism (BiLSTM-ATT) to capture the most important semantic information in the sentence; Finally, the conditional random field (CRF) is used to learn the dependence between adjacent tags to obtain the global optimal sentence level tag sequence. The experimental results show that the proposed model achieves state-of-the-art performance on both Microsoft Research Asia (MSRA) corpus and people’s daily corpus, with F1 values of 94.77% and 95.97% respectively.

Cloud computing faces a series of challenges, such as insufficient bandwidth, unsatisfactory real-time, privacy protection, and energy consumption. To overcome the challenges, edge computing emerges. Edge computing refers to a process where the open platform that converges the core capabilities of networks, computing, storage, and applications provides intelligent services at the network edge near the source of the objects or data to meet the critical requirements for agile connection, real-time services, data optimization, application intelligence, security and privacy protection of industry digitization. Edge computing consists of three elements: edge, computing, and intelligence. Edge computing and the Internet of Things (IoT) mutually create, and edge computing and cloud computing complement each other. In the architecture of edge computing, resources are distributed to the edge nodes, and therefore the storage system is near users while the computation function is near data. In this way, the stress on the backbone network can be lessened. With this architecture, the existing key technologies for computation, networks, and storage will change significantly. ZTE’s edge computing solutions can ensure the service quality of operators and greatly enhance the experience of mobile users.