PocketWeb

Abstract

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 browsing 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 machine learning approach based on stochastic gradient boosting techniques 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.

Energy-Saving Strategies for Mobile Web Apps and their Measurement: Results from a Decade of Research

“…The mobile device: In past experiments, Android devices have been commonly used for measurements, with Samsung [14], [28], [29], [34], [36], [40], [42], [46], [51], [57], [58], [62] and Nexus [26], [31], [32], [35], [37], [41], [44], [51]- [53], [61], [63] devices being the most popular ones. The Android versions differed from Android 4 to 11.…”

“…In three cases, the Batterystats tool was used. Batterystats collects battery data (based on Smartphone [3], [30]- [46] [3], [14], [16], [28]- [30], [32], [37], [40], [42]- [53], [53]- [65] Table V: Energy measurement approaches.…”

“…[3], [16], [26], [27], [30], [31], [31]- [34], [39], [43], [46], [47], [49], [50], [59] Hardware-based 24 [14], [15], [28], [29], [35], [37], [38], [40], [41], [44], [45], [51]- [56], [58], [60], [62]- [65], [67] Estimation-based 4 [36], [42], [48], [61] supplier profiles) on the device and can be accessed via the Android Debug Bridge, for further analysis, even on a single hardware unit [30], [32], [33]. An alternative for devices with Qualcomm chips was Trepn Profiler [31], [32], [49], [50].…”

“…Caching 3 [33], [39], [42] (Pre-)filtering of web-content 8 [28], [37], [40], [41], [43]- [45], [63] Adoption of web content…”

“…The avoidance of transmitting similar content again can reduce energy consumption. Before the emergence of PocketWeb in 2012 by Dimitrios Lymberopoulos et al [42], caching techniques were primarily energy-efficient when dealing with static web content on mobile devices.…”