Abstract
Modeling the energy consumption of applications on mobile devices is an important topic that has received much attention in recent years. However, there has been very little research on modeling the energy consumption of the mobile Web. This is primarily due to the short-lived yet complex page load process that makes it infeasible to rely on coarse-grained resource monitoring for accurate power estimation.
We present RECON, a modeling approach that accurately estimates the energy consumption of any Web page load and deconstructs it into the energy contributions of individual page load activities. Our key intuition is to leverage low-level application semantics in addition to coarse-grained resource utilizations for modeling the page load energy consumption. By exploiting fine-grained information about the individual activities that make up the page load, RECON enables fast and accurate energy estimations without requiring complex models. Experiments across 80 Web pages and under four different optimizations show that RECON can estimate the energy consumption for a Web page load with an average error of less than 7%. Importantly, RECON helps to analyze and explain the energy effects of an optimization on the individual components of Web page loads.
- 2011. How loading time affects bottomline. https://blog.kissmetrics.com/loading-time. (2011).Google Scholar
- 2012. 75% of Developers Using HTML5: Survey. http://www.eweek.com/c/a/Application-Development/75 of-Developers-Using- HTML5-Survey-508096. (2012).Google Scholar
- 2012. How one second could cost Amazon 1.6 billion in sales. http://www.fastcompany.com/1825005/how-one-second-could-cost-amazon-16-billion-sales. (2012).Google Scholar
- 2014. BSDGeek Jake Ad Blocker. http://androidforums.com/threads/guide-best-ad-blocking-no-appneeded-for-rooted-phone.853586/. (2014).Google Scholar
- 2015. Google chrome promises longer battery life and fast performance, eyes Safari. http://www.techtimes.com/articles/60277/20150614/googles-mac-friendly-chrome-promises-faster-performance-longer-battery-life.htm. (2015).Google Scholar
- 2015. Google promises update as users suffer. http://www.technobuffalo.com/2015/06/12/google-promises-chrome-updates-as-users-suffer/. (2015).Google Scholar
- 2015. Monsoon Power Monitor. http://msoon.github.io/powermonitor/. (2015).Google Scholar
- 2015. No, Apps Aren't Winning. The Mobile Browser Is. http://marketingland.com/morgan-stanley-noapps-arent-winning-the-mobile-browser-is-144303. (2015).Google Scholar
- 2015. Page Speed Insight Rules. https://developers.google.com/speed/docs/insights/rules?hl=en. (2015).Google Scholar
- 2017. Calabash. http://calaba.sh. (2017).Google Scholar
- 2017. Chrome Developer Tools. https://developers.google.com/web/tools/chrome-devtools/?hl=en. (2017).Google Scholar
- 2017. Code and relevant scripts for RECON. https://github.com/davycao/Deconstructing-Mobipower. (2017).Google Scholar
- 2017. Google Pagespeed Insights. https://developers.google.com/speed/pagespeed/insights. (2017).Google Scholar
- 2017. mod_pagespeed. http://www.modpagespeed.com/. (2017).Google Scholar
- 2017. Module ngx_http_gzip_module. http://nginx.org/en/docs/http/ngx_http_gzip_module.html. (2017).Google Scholar
- 2017. Safari: Longer battery life and faster performance. http://www.apple.com/safari/. (2017).Google Scholar
- 2017. SPDY. https://www.chromium.org/spdy/spdy-whitepaper. (2017).Google Scholar
- 2017. The top 500 sites on the web. http://www.alexa.com/topsites/. (2017).Google Scholar
- Victor Agababov, Michael Buettner, Victor Chudnovsky, Mark Cogan, Ben Greenstein, Shane McDaniel, Michael Piatek, Colin Scott, Matt Welsh, and Bolian Yin. 2015. Flywheel: Google's Data Compression Proxy for the Mobile Web. In Proceedings of the 12th USENIX Conference on Networked Systems Design and Implementation (NSDI '15). Oakland, CA, USA, 367--380. https://www.usenix.org/conference/nsdi15/technical-sessions/presentation/agababov Google Scholar
Digital Library
- Niranjan Balasubramanian, Aruna Balasubramanian, and Arun Venkataramani. 2009. Energy consumption in mobile phones: a measurement study and implications for network applications. In Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference (IMC '09). Chicago, Illinois, USA, 280--293. Google Scholar
Digital Library
- Michael Butkiewicz, Daimeng Wang, Zhe Wu, Harsha V. Madhyastha, and Vyas Sekar. 2015. Klotski: Reprioritizing Web Content to Improve User Experience on Mobile Devices. In Proceedings of the 12th USENIX Conference on Networked Systems Design and Implementation (NSDI '15). Oakland, CA, USA, 439--453. Google Scholar
Digital Library
- Aaron Carroll and Gernot Heiser. 2010. An Analysis of Power Consumption in a Smartphone. In Proceedings of the 2010 USENIX Conference on USENIX Annual Technical Conference (USENIX ATC '10). Boston, MA, USA, 21--21. http://dl.acm.org/citation.cfm?id=1855840.1855861 Google Scholar
Digital Library
- Xiaomeng Chen, Ning Ding, Abhilash Jindal, Y Charlie Hu, Maruti Gupta, and Rath Vannithamby. 2015. Smartphone energy drain in the wild: Analysis and implications. ACM SIGMETRICS Performance Evaluation Review 43, 1 (2015), 151--164. Google Scholar
Digital Library
- Padraig Cunningham, John Carney, and Saji Jacob. 2000. Stability problems with artificial neural networks and the ensemble solution. Artificial Intelligence in Medicine 20, 3 (2000), 217--225. Google Scholar
Digital Library
- Philip Dixon. 2009. Shopzilla's Site Redo - You Get What You Measure. In 2009 Web Performance and Operations Conference (Velocity). San Jose, CA, USA.Google Scholar
- Mian Dong and Lin Zhong. 2011. Self-constructive high-rate system energy modeling for battery-powered mobile systems. In Proceedings of the 9th international conference on Mobile systems, applications, and services (MobiSys '11). Bethesda, Maryland, USA, 335--348. Google Scholar
Digital Library
- Simon Haykin. 2004. Neural Networks: A Comprehensive Foundation (3rd ed.). Prentice Hall PTR, Upper Saddle River, NJ, USA. Google Scholar
Digital Library
- Jeff Heaton. 2008. Introduction to Neural Networks for Java (2nd ed.). Heaton Research, Inc., St.Louis, MO, USA. Google Scholar
Digital Library
- Radhika Mittal, Aman Kansal, and Ranveer Chandra. 2012. Empowering developers to estimate app energy consumption. In Proceedings of the 18th annual international conference on Mobile computing and networking (MobiCom '12). Istanbul, Turkey. Google Scholar
Digital Library
- Stephen G. Nash and Jorge Nocedal. 1991. A Numerical Study of the Limited Memory BFGS Method and the Truncated-Newton Method for Large Scale Optimization. SIAM Journal on Optimization 1, 3 (1991), 358--372.Google Scholar
Cross Ref
- Javad Nejati and Aruna Balasubramanian. 2016. An In-depth Study of Mobile Browser Performance. In Proceedings of the 25th International Conference on World Wide Web (WWW '16). Montreal, Quebec, Canada, 1305--1315. Google Scholar
Digital Library
- Ravi Netravali, James Mickens, and Hari Balakrishnan. 2016. Polaris: Faster Page Loads Using Fine grained Dependency Tracking. In Proceedings of the 13th USENIX Symposium on Networked Systems Design and Implementation (NSDI'16). Santa Clara, CA, USA, 123--136. Google Scholar
Digital Library
- Abhinav Pathak, Y Charlie Hu, Ming Zhang, Paramvir Bahl, and Yi-Min Wang. 2011. Fine-grained power modeling for smartphones using system call tracing. In Proceedings of the sixth conference on Computer systems (EuroSys '11). Salzburg, Austria, 153--168. Google Scholar
Digital Library
- Feng Qian, Zhaoguang Wang, Alexandre Gerber, Zhuoqing Mao, Subhabrata Sen, and Oliver Spatscheck. 2011. Profiling Resource Usage for Mobile Applications: A Cross-layer Approach. In Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services (MobiSys '11). Bethesda, Maryland, USA, 321--334. Google Scholar
Digital Library
- R Development Core Team. 2008. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.orgGoogle Scholar
- Kent Rasmussen, Alex Wilson, and Abram Hindle. 2014. Green Mining: Energy Consumption of Advertisement Blocking Methods. In Proceedings of the 3rd International Workshop on Green and Sustainable Software (GREENS '14). Hyderabad, India, 38--45. Google Scholar
Digital Library
- Alex Shye, Benjamin Scholbrock, and Gokhan Memik. 2009. Into the wild: studying real user activity patterns to guide power optimizations for mobile architectures. In Proceedings of the 42nd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO-42). New York, NY, USA, 168--178. Google Scholar
Digital Library
- Donald Specht. 1991. A general regression neural network. IEEE Transactions on Neural Networks 2, 6 (1991), 568--576. Google Scholar
Digital Library
- Xiao Sophia Wang, Aruna Balasubramanian, Arvind Krishnamurthy, and David Wetherall. 2013. Demystifying Page Load Performance with WProf.. In Proceedings of the 10th USENIX conference on Networked Systems Design and Implementation (NSDI'13). Lombard, IL, USA, 473--485. Google Scholar
Digital Library
- Xiao Sophia Wang, Aruna Balasubramanian, Arvind Krishnamurthy, and David Wetherall. 2014. How Speedy is SPDY?. In Proceedings of the 11th USENIX Conference on Networked Systems Design and Implementation (NSDI '14). Seattle, WA, USA, 387--399. http://dl.acm.org/citation.cfm?id=2616448.2616484 Google Scholar
Digital Library
- Fengyuan Xu, Yunxin Liu, Qun Li, and Yongguang Zhang. 2013. V-edge: Fast Self-constructive Power Modeling of Smartphones Based on Battery Voltage Dynamics.. In Proceedings of the 10th USENIX conference on Networked Systems Design and Implementation (NSDI'13), Vol. 13. Lombard, IL, USA, 43--56. Google Scholar
Digital Library
- Chanmin Yoon, Dongwon Kim, Wonwoo Jung, Chulkoo Kang, and Hojung Cha. 2012. AppScope: Application Energy Metering Framework for Android Smartphones Using Kernel Activity Monitoring. In Proceedings of the 2012 USENIX Conference on Annual Technical Conference (USENIX ATC '12). Boston, MA, USA, 36--36. http://dl.acm.org/citation.cfm?id=2342821.2342857 Google Scholar
Digital Library
- Lide Zhang, Birjodh Tiwana, Zhiyun Qian, Zhaoguang Wang, Robert P Dick, Zhuoqing Morley Mao, and Lei Yang. 2010. Accurate online power estimation and automatic battery behavior based power model generation for smartphones. In Proceedings of the Eighth IEEE/ACM/IFIP International Conference on Hardware/software Codesign and System Synthesis (CODES/ISSS '10). Scottsdale, Arizona, USA, 105--114. Google Scholar
Digital Library
- Yifan Zhang, Xudong Wang, Xuanzhe Liu, Yunxin Liu, Li Zhuang, and Feng Zhao. 2013. Towards Better CPU Power Management on Multicore Smartphones. In Proceedings of the Workshop on Power-Aware Computing and Systems (HotPower '13). Farmington, PA, USA. Google Scholar
Digital Library
- Yuhao Zhu and Vijay Janapa Reddi. 2013. High-performance and energy-efficient mobile web browsing on big/little systems. In Proceedings of the 2013 IEEE 19th International Symposium on High Performance Computer Architecture (HPCA '13). Shenzhen, China, 13--24. Google Scholar
Digital Library
- Yuhao Zhu and Vijay Janapa Reddi. 2014. WebCore: Architectural Support for Mobile Web Browsing. In Proceeding of the 41st annual international symposium on Computer architecuture (ISCA '14). Minneapolis, MN, USA, 541--552. Google Scholar
Digital Library
Index Terms
Deconstructing the Energy Consumption of the Mobile Page Load
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