eBrowser: Making Human-Mobile Web Interactions Energy Efficient with Event Rate Learning

Xu, Fei; Yang, Shuai; Zhou, Zhi; Rao, Jia

doi:10.1109/icdcs.2018.00058

Cited by 5 publications

(20 citation statements)

References 20 publications

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“…Rather than retraining on the entire training dataset, transfer learning uses only a dozen of webpages. This not only significantly reduces the profiling overhead but also allows performing learning on the user's device to mitigate the privacy concern for doing that on a remote server [12]. To detect and improve ageing models, CAMEL uses conformal predictions (Section V-B) to assess the credibility of each prediction.…”

Section: Adaptive Learningmentioning

confidence: 99%

“…We ask each user to watch the screen update of each training webpage on a XiaoMi 9 smartphone under various FPS speeds. We also vary the incoming event by considering 5 commonly interactive speeds per gesture [12] To help our participants to correlate the generated events to finger movements, we invite them to interact with the device and show the resultant FPS of their finger movements. For each training instance, we ask a user to select the lowest acceptable screen update rate.…”

Section: A Problem Modeling and Training Data Generationmentioning

confidence: 99%

“…Some more recent works try to reduce the energy footprint of web interactions by limiting the processor clock speed [6], [7], [11] or dropping some interaction events [12]. However, these solutions are suboptimal as they achieve energy savings at the cost of user experience.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Camel: Smart, Adaptive Energy Optimization for Mobile Web Interactions

Ren

Yuan

Nurmi

et al. 2020

IEEE INFOCOM 2020 - IEEE Conference on Computer Communications

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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.

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Section: Adaptive Learningmentioning

confidence: 99%

Section: A Problem Modeling and Training Data Generationmentioning

confidence: 99%

See 1 more Smart Citation

Camel: Smart, Adaptive Energy Optimization for Mobile Web Interactions

Ren

Yuan

Nurmi

et al. 2020

IEEE INFOCOM 2020 - IEEE Conference on Computer Communications

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“…State-of-the-art. We compare our approach against eBrowser [6], the most closely related recent work. eBrowser reduces the energy consumption for a given user event by putting the rendering process into sleep for some time.…”

Section: Baseline and Competitive Approachmentioning

confidence: 99%

“…Some of the more recent studies like PES [5] and eBrowser [6] have attempted to address the energy optimization problem for interactive mobile web browsing. While promising, they have critical drawbacks.…”

Section: Introductionmentioning

confidence: 99%

Using Machine Learning to Optimize Web Interactions on Heterogeneous Mobile Systems

et al. 2019

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The web has become a ubiquitous application development platform for mobile systems. Yet, web access on mobile devices remains an energy-hungry activity. Prior work in the field mainly focuses on the initial page loading stage, but fails to exploit the opportunities for energy-efficiency optimization while the user is interacting with a loaded page. This paper presents a novel approach for performing energy optimization for interactive mobile web browsing. At the heart of our approach is a set of machine learning models, which estimate at runtime the frames per second for a given user interaction input by running the computation-intensive web render engine on a specific processor core under a given clock speed. We use the learned predictive models as a utility function to quickly search for the optimal processor setting to carefully trade responsive time for reduced energy consumption. We integrate our techniques to the opensource Chromium browser and apply it to two representative mobile user events: scrolling and pinching (i.e., zoom in and out). We evaluate the developed system on the landing pages of the top-100 hottest websites and two big.LITTLE heterogeneous mobile platforms. Our extensive experiments show that the proposed approach reduces the system-wide energy consumption by over 36% on average and up to 70%. This translates to an over 17% improvement on energy-efficiency over a state-of-the-art event-based web browser scheduler, but with significantly fewer violations on the quality of service.

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

Benedikt

Felderer

2023

2023 IEEE/ACM 10th International Conference on Mobile Software Engineering and Systems (MOBILESoft)

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In 2022, over half of the web traffic was accessed through mobile devices. By reducing the energy consumption of mobile web apps, we can not only extend the battery life of our devices, but also make a significant contribution to energy conservation efforts. For example, if we could save only 5% of the energy used by web apps, we estimate that it would be enough to shut down one of the nuclear reactors in Fukushima. This paper presents a comprehensive overview of energy-saving experiments and related approaches for mobile web apps, relevant for researchers and practitioners. To achieve this objective, we conducted a systematic literature review and identified 44 primary studies for inclusion. Through the mapping and analysis of scientific papers, this work contributes: (1) an overview of the energy-draining aspects of mobile web apps, (2) a comprehensive description of the methodology used for the energy-saving experiments, and (3) a categorization and synthesis of various energy-saving approaches.

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eBrowser: Making Human-Mobile Web Interactions Energy Efficient with Event Rate Learning

Cited by 5 publications

References 20 publications

Camel: Smart, Adaptive Energy Optimization for Mobile Web Interactions

Camel: Smart, Adaptive Energy Optimization for Mobile Web Interactions

Using Machine Learning to Optimize Web Interactions on Heterogeneous Mobile Systems

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

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