Name
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-59072-1_63 | ADVANCES IN NEURAL NETWORKS, PT I |
Keywords | Field | DocType |
LSTM,Deep learning,Bayesian model,Web Caching,Resource prediction | Computer science,Cache,Software,Artificial intelligence,Deep learning,Soft computing,Dependency graph,Distributed computing,Bayesian network,Robot,Machine learning,Database,Web server | Conference |
Volume | ISSN | Citations |
10261 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ning Xie | 1 | 70 | 9.29 |
| Kyle A. Brown | 2 | 0 | 3.04 |
| Nathan Rude | 3 | 5 | 1.13 |
| Derek Doran | 4 | 0 | 0.34 |