Improving Web Browsing Experience with Personalized Edge Computing

Autor: Zhaoxin Wu, Yasushi Shinjo
Rok vydání: 2019
Předmět:
Zdroj: 2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS).
Popis: In recent years, webpages are becoming complex rapidly and their loading times are also becoming longer. This paper tackles this problem with personalized edge computing. In typical edge computing, an edge server collaborates with cloud web servers. In personalized edge computing, on the other hand, an edge server called an Edge Server in the Middle (ESM) collaborates with users' mobile devices. Based on personalized edge computing, this paper focuses on two techniques: edge aided caching and edge aided reprioritizing. Edge aided caching reduces the page loading time on mobile devices because an ESM automatically keeps the cached components up to date. Edge aided reprioritizing forces a web browser to show visual components earlier and reduces the white screen time. The ESM also uses HTTP/2 instead of HTTP/1.1. This reduces the number of interactions between a mobile device and the ESM, and makes it possible to use advanced features such as server push and priority. Edge aided caching has been implemented in a PC for the web browser Google Chrome for Android. An experimental result shows that edge aided caching reduced the page loading time of a popular webpage by 59% in a crowded network condition. Another experimental result shows that edge aided reprioritizing reduced the white screen time of a webpage with many photo images by 21%.
Databáze: OpenAIRE