Automated energy optimization of HTTP requests for mobile applications

Li, Ding; Lyu, Yingjun; Gui, Jiaping; Halfond, William G. J.

doi:10.1145/2884781.2884867

Cited by 91 publications

(59 citation statements)

References 41 publications

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“…There is a wide range of activities on exploiting compiler-based code optimization [12], [13], runtime task scheduling [14], [15], or a combination of both [7] to optimize different workloads for energy efficiency. Other relevant work in web browsing optimization exploits application knowledge to batch network communications [16], [17], and parallel downloading [18], which primarily target the initial page loading phase. Our work is complementary to prior works by targeting the low-level optimization, and we do so by utilizing the hardware configuration knobs to perform energy optimization during the interacting phase.…”

Section: A Energy Optimizationmentioning

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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Section: A Energy Optimizationmentioning

confidence: 99%

Using Machine Learning to Optimize Web Interactions on Heterogeneous Mobile Systems

et al. 2019

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“…According to Statcounter, most of the traffic of mobile devices is based on Hyper Text Transfer Protocol (HTTP) [4].Among all kinds of network operations, those related with HTTP are the most energy consuming, representing almost 80% of the global network related energy consumption [5]. Therefore, reducing the power expenditure of browser data transfer can have a significant impact on the overall energy consumption of the device.…”

Section: Introductionmentioning

confidence: 99%

An Empirical Study of Power Consumption of Web-Based Communications in Mobile Phones

Ayala

Amor

Fuentes

et al. 2017

2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing,

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Abstract-Currently, mobile devices are the most popular pervasive computing device, and they are becoming the primer way for Web access. Energy is a critical resource in such pervasive computing devices, being network communication one of the primary energy consuming operations in mobile apps. Indeed, web-based communication is the most used, but also energy demanding. So, mobile web developers should be aware of how much energy consumes the different web-based communication alternatives. The goal of this paper is to measure and compare the energy consumption of three asynchronous Web-based methods in mobile devices. Our experiments consider three different Web applications models that allow a web server to push data to a browser: Polling, Long Polling and WebSockets. The obtained results are analyzed to get more accurate understanding of the impact in energy consumption of a mobile browser for each of these three methods. The utility of these experiments is to show developers what are the factors that influence the energy consumption when different web-based asynchronous communication is used. With this information mobile web developers could reduce the power consumption of web applications on mobile devices, by selecting the most appropriate method for asynchronous server communication.

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“…Making HTTP requests is one of the most energy consuming activities in Android apps, Li et al [18] propose an approach to reducing the energy consumption of HTTP requests by automatically detecting and then bundling multiple HTTP requests.…”

Section: Optimizing Energymentioning

confidence: 99%

Detecting Energy Bugs in Android Apps Using Static Analysis

Jiang

Yang

Qin

et al. 2017

Formal Methods and Software Engineering

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Abstract. Energy bugs in Android apps are defects that can make Android systems waste much energy as a whole. Energy bugs detection in Android apps has become an important issue since smartphones usually operate on a limited amount of battery capacity and the existence of energy bugs may lead to serious drain in the battery power. This paper focuses on detecting two types of energy bugs, namely resource leak and layout defect, in Android apps. A resource leak is a particular type of energy wasting phenomena where an app does not release its acquired resources such as a sensor and GPS. A layout defect refers to a poor layout structure causing more energy consumption for measuring and drawing the layout. In this paper, we present a static analysis technique called SAAD, that can automatically detect energy bugs in a context-sensitive way. SAAD detects the energy bugs by taking an inter-procedural anaysis of an app. For resource leak analysis, SAAD decompiles APK file into Dalvik bytecodes files and then performs resource leak analysis by taking components call relationship analysis, inter-procedure and intra-procedure analysis. For detecting layout defect, SAAD firstly employs Lint to perform some traditional app analysis, then filters energy defects from reported issues. Our experimental result on 64 publicly-available Android apps shows that SAAD can detect energy bugs effectively. The accuracies of detecting resource leak and layout energy defect are 86.67% and 76.27% respectively.

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Automated energy optimization of HTTP requests for mobile applications

Cited by 91 publications

References 41 publications

Using Machine Learning to Optimize Web Interactions on Heterogeneous Mobile Systems

Using Machine Learning to Optimize Web Interactions on Heterogeneous Mobile Systems

An Empirical Study of Power Consumption of Web-Based Communications in Mobile Phones

Detecting Energy Bugs in Android Apps Using Static Analysis

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