Proxy-basedWeb Prefetching Exploiting Long Short-Term Memory

被引：1

作者：

Won, Jiwoong ^{[1
]}

Zou, Wenbo ^{[2
]}

Ahn, Jemin ^{[1
]}

Lim, Jiseoup ^{[3
]}

Kim, Gun-Woo ^{[4
]}

Kang, Kyungtae ^{[1
]}

机构：

[1] Hanyang Univ, Dept Comp Sci & Engn, Seoul, South Korea

[2] Bytedance, Beijing, Peoples R China

[3] Hanyang Univ, Dept Appl Artificial Intelligence, Seoul, South Korea

[4] Gyeongsang Natl Univ, Sch Comp Sci, Jinju, South Korea

来源：

38TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING, SAC 2023 | 2023年

关键词：

Web Prefetching; Deep Learning; LSTM;

D O I：

10.1145/3555776.3577865

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

We propose an intention-related long short-term memory (Ir-LSTM) model based on deep learning to realize web prediction. This model draws on an LSTM model and skip-gram embedding method, and we expand the input features with user information. To maximize its potential, we propose a real-time dynamic allocation module that detects traffic bursts in real time and ensures better utilization of server resources. Experiments demonstrated that Ir-LSTM can improve the hit ratio by approximately 27% rather than hidden Markov model (HMM) and pure LSTM.

引用

页码：1831 / 1834

页数：4

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