@inproceedings{65d33e9ec27646bb9f85597de81f9ed6,
title = "A Soft Computing Prefetcher to Mitigate Cache Degradation by Web Robots",
abstract = " This paper investigates the feasibility of a resource prefetcher able to predict future requests made by web robots, which are software programs rapidly overtaking human users as the dominant source of web server traffic. Such a prefetcher is a crucial first line of defense for web caches and content management systems that must service many requests while maintaining good performance. Our prefetcher marries a deep recurrent neural network with a Bayesian network to combine prior global data with local data about specific robots. Experiments with traffic logs from web servers across two universities demonstrate improved predictions over a traditional dependency graph approach. Finally, preliminary evaluation of a hypothetical caching system that incorporates our prefetching scheme is discussed.",
keywords = "Bayesian model, Deep learning, LSTM, Resource prediction, Web caching",
author = "Ning Xie and Kyle Brown and Nathan Rude and Derek Doran",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 14th International Symposium on Neural Networks, ISNN 2017 ; Conference date: 21-06-2017 Through 26-06-2017",
year = "2017",
doi = "10.1007/978-3-319-59072-1_63",
language = "English",
isbn = "9783319590714",
volume = "10261",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "536--546",
editor = "Andrew Leung and Fengyu Cong and Qinglai Wei",
booktitle = "Advances in Neural Networks",
}