Ren, J, Yuan, L, Nurmi, P et al. (6 more authors) (2020) Camel: Smart, Adaptive Energy Optimization for Mobile Web Interactions. In: Proceedings of the IEEE INFOCOM 2020 - IEEE Conference on Computer Communications. IEEE International Conference on Computer Communications (INFOCOM), 06-09 Jul 2020, Toronto, ON, Canada. IEEE , pp. 119-128. ISBN 978-1-7281-6412-0
Abstract
Web technology underpins many interactive mobile applications. However, energy-efficient mobile web interactions is an outstanding challenge. Given the increasing diversity and complexity of mobile hardware, any practical optimization scheme must work for a wide range of users, mobile platforms and web workloads. This paper presents CAMEL, a novel energy optimization system for mobile web interactions. CAMEL leverages machine learning techniques to develop a smart, adaptive scheme to judiciously trade performance for reduced power consumption. Unlike prior work, CAMEL directly models how a given web content affects the user expectation and uses this to guide energy optimization. It goes further by employing transfer learning and conformal predictions to tune a previously learned model in the end-user environment and improve it over time. We apply CAMEL to Chromium and evaluate it on four distinct mobile systems involving 1,000 testing webpages and 30 users. Compared to four state-of-the-art web-event optimizers, CAMEL delivers 22% more energy savings, but with 49% fewer violations on the quality of user experience, and exhibits orders of magnitudes less overhead when targeting a new computing environment.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 17 Jan 2020 14:48 |
Last Modified: | 01 Oct 2020 12:56 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/INFOCOM41043.2020.9155489 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:155720 |