Abstract: Industrial applications usually have strict requirements of data transmission certainty. It is therefore essential for industrial edge computing applications to deploy a proper caching strategy at edge nodes, in order to ensure the real-time performance guarantee. The cache optimization problem is formulized considering the specific requirements of industrial applications. The content request is modeled as shot noise model (SNM) to reflect the dynamic characteristics of popularity. A scheme of popularity prediction is then proposed by defining a feature similarity function of the requested content set in the latest periodic time window. Based on it, a new cache replacement algorithm called combing periodic popularity prediction and size caching strategy (PPPS) is proposed. The value of each cache content is determined together with the popularity, size and time updates parameters. The content with minimum value will be deleted with the highest priority when content replacement happens. The experimental results show that the proposed PPPS algorithm outperforms all the 5 baseline algorithms, which are the most popular content (MPC), greedy dual size (GDS), least recently used (LRU), least frequently used (LFU), and FIFO algorithm. PPPS algorithm obtains the best performance of hit rate and the average delay in all the testing cases with different parameter settings on user request models, content size distributions, and content types.