Research on Data Cache Optimization Based on Time Series State Prediction

Data cache can reduce network congestion in a certain extent, and it can also reduce server load and users access delay. However, the data cache is just passable in the cache hit rate and byte hit rate. It cannot play very well to accelerate query tasks response effect. Combining time series prediction method, this paper tries to predict the state of data using Autoregressive Integrated Moving Average Model and proposes a new cache strategy with Naive Bayes Classifier. Experimental results show that the new cache strategy is superior to the ID3 decision tree and BP neural network classifier in the precision and recall index. And compared with LRU algorithm, optimized cache strategy cannot only improve the cache efficiency, but also effectively improve the request hit rate of data cache.