A.: Pocketweb: instant web browsing for mobile devices

The high network latencies and limited battery life of mobile phones can make mobile web browsing a frustrating experience. In prior work, we proposed trading memory capacity for lower web access latency and a more convenient data transfer schedule from an energy perspective by prefetching slowly-changing data (search queries and results) nightly, when the phone is charging. However, most web content is intrinsically much more dynamic and may be updated multiple times a day, thus eliminating the effectiveness of periodic updates. This paper addresses the challenge of prefetching dynamic web content in a timely fashion, giving the user an instant web brows-ing experience but without aggravating the battery lifetime issue. We start by analyzing the web access traces of 8,000 users, and observe that mobile web browsing exhibits a strong spatiotemporal signature, which is different for every user. We propose to use a ma-chine learning approach based on stochastic gradient boosting tech-niques to efficiently model this signature on a per user basis. The machine learning model is capable of accurately predicting future web accesses and prefetching the content in a timely manner. Our experimental evaluation with 48,000 models trained on real user datasets shows that we can accurately prefetch 60 % of the URLs for about 80-90 % of the users within 2 minutes before the request. The system prototype we built not only provides more than 80% lower web access time for more than 80 % of the users, but it also achieves the same or lower radio energy dissipation by more than 50 % for the majority of mobile users