Focused on the issue that the mass small file system developed based on HDFS (Hadoop Distributed File System), SMDFS (Mass Small Distributed File System), is not compatible with POSIX (Portable Operating System Interface of UNIX) constraints, a POSIX compatible technology based on local cache and an efficient metadata management technology based on temporary data cache were proposed. Firstly, the data storage area was set to realize the redirection of the file flow in the read-write mode, and then an asynchronous thread pool model was established to synchronize the data in temporary cache, thereby completing all POSIX-related file operations from the user layer to the storage layer. In addition, with the help of the metadata cache of the skip list structure, the efficiency of metadata operations such as the List directory was optimized. The test results show that, compared to the Linux client of HDFS, the performance of random read improves ten times more, the sequential read and sequential write improves about three to four times. The performance of random write can reach 20% of the local file system. Besides, the List operation efficiency of the directory improves about 10 times. However, due to the additional switching of kernel-mode and user-mode introduced by FUSE (Filesystem in Userspace), the Linux client of SMDFS3.0 has a performance penalty of about 50% compared to Java interface.

A model was built to evaluate the customer value in terms of current value and potential value. This model used the Analytic Network Process (ANP) for weighting which considered the interrelationship among indexes, then calculated the customer value based on the weight and score of the indexes and then classified the customers by K-means. Taking a tobacco company for example at the end of this paper, qualitative and quantitative method was used to establish a customer value evaluation index system, ANP was used to weight indexes and classify the customers by K-means based on the evaluation result, and the marketing strategy of each customer group was analyzed at last. The proposed model can evaluate and classify the customer value more comprehensively and objectively.