Research on hybrid Markov prediction model based on clustering

Wei Niu; Juan Cheng; Dongxu Sun; Xiaogang Song

doi:10.1117/12.2639741

19 May 2022 Research on hybrid Markov prediction model based on clustering

Wei Niu, Juan Cheng, Dongxu Sun, Xiaogang Song

Proceedings Volume 12250, International Symposium on Computer Applications and Information Systems (ISCAIS 2022); 122500A (2022) https://doi.org/10.1117/12.2639741
Event: International Symposium on Computer Applications and Information Systems (ISCAIS2022), 2022, Shenzhen, China

Abstract

In order to reduce the network load and the access delay of web users, caching and prefetching technology are considered to be the main methods to solve the problems of web access delay. At present, the research on caching and prefetching technology often only emphasizes the improvement of replacement strategy or prefetching algorithm, lacks the research on prefetching method, and the improvement of access delay is not ideal. Combined with caching and prefetching methods, a hybrid Markov caching and prefetching model considering the change of user browsing trend over time is established. Aiming at the shortcomings of the current web page prefetching methods, the model uses the hierarchical clustering method to find out the time-varying interval of user browsing behavior. Each time interval uses a Markov chain to predict and maintain the freshness of information. The experimental results show that the proposed method can improve the cache hit rate and reduce the network access delay.

Citation Download Citation

Wei Niu, Juan Cheng, Dongxu Sun, and Xiaogang Song "Research on hybrid Markov prediction model based on clustering", Proc. SPIE 12250, International Symposium on Computer Applications and Information Systems (ISCAIS 2022), 122500A (19 May 2022); https://doi.org/10.1117/12.2639741

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