Web prefetching is a key technology to hide network latencies from users. Conventional prefetching methods, however, misconstrue the purpose of user's browsing behaviors and resulting experience due to their dependence on statistical characteristics or metadata of individual Web applications. In this letter, we propose a predictive prefetching scheme, WebPrefetcher, which utilizes interaction events to decipher user's genuine intention and context. Our intensive performance analysis results obtained with a real Web browser demonstrate that WebPrefetcher improves user-perceived quality of experience noticeably, outperforming competitive models.
Bibliographical noteFunding Information:
Manuscript received September 17, 2020; revised October 23, 2020; accepted November 11, 2020. Date of publication November 16, 2020; date of current version March 10, 2021. This research was partly supported by the NRF grant funded by the Korea government (MSIT) (No. 2019R1A2C2088812) and Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (No. 2017M3C4A7083676). The associate editor coordinating the review of this letter and approving it for publication was W. Cerroni. (Corresponding author: Wonjun Lee.) The authors are with the Network and Security Research Laboratory, School of Cybersecurity, Korea University, Seoul 02841, South Korea (e-mail: email@example.com). Digital Object Identifier 10.1109/LCOMM.2020.3038255
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- Web prefetching
- quality of experience
- user interaction
ASJC Scopus subject areas
- Modelling and Simulation
- Computer Science Applications
- Electrical and Electronic Engineering