With the growing demand of big data computing, the processing speed of the cluster needs to be improved rapidly. However, the processing performance of the existing big data framework can not satisfy the requirement of the computing development gradually. As the framework of the storage is distributed, the placement of data to be processed has become one of the key factors affecting the performance of the cluster. Firstly, the current distributed file system structure was introduced. Then the popular data placement algorithms were summarized and classified according to different optimization goals, such as network load balance, energy saving and fault tolerance. Finally, future challenges and research directions in the area of storage framework and algorithms were presented.

To select the optimal threshold for image segmentation, a new method based on local complexity and local difference was proposed. Firstly, the local grayscale features of a given image were generated, including local complexity and local difference. Next, the new feature matrix was constructed using local feature fusion. Then, an automatic threshold was defined based on the mean and standard deviation of feature matrix, and the image transition region was extracted. Finally, the optimal grayscale threshold was obtained by calculating the grayscale mean of transition pixels, and the binary result was yielded. The experimental results show that, the proposed method performs well in transition region extraction and thresholding, and it is reasonable and effective. It can be an alternative to traditional methods.