关键词: Web缓存, 替换策略, 分区域特征提取, 朴素贝叶斯分类器, 重访概率预测

Abstract: Web caching is used to solve network access delays and network congestion problems. The cache replacement strategy directly affects the cache hit rate. A Web cache replacement strategy based on Naive Bayes（NB） classifier for re-access probability prediction is proposed. Firstly, according to the user’s previous access log, multiple features are extracted by the partition operation to represent the object accessed, and the feature data set is constructed. Then, the NB classifier is trained to determine the probability that the objects in the cache are accessed again, and assigning weights to the objects. Finally, it combines the LRU strategy to reasonably delete some objects. The simulation results show that the proposed strategy effectively reduces the execution time while ensuring a high hit rate.

Key words: Web cache, replacement strategy, partition feature extraction, Naive Bayes（NB） classifier, re-access probability prediction