Abstract
Web computing is gradually shifting toward mobile devices, in which the energy budget is severely constrained. As a result, Web developers must be conscious of energy efficiency. However, current Web languages provide developers little control over energy consumption. In this paper, we take a first step toward language-level research to enable energy-efficient Web computing. Our key motivation is that mobile systems can wisely budget energy usage if informed with user quality-of-service (QoS) constraints. To do this, programmers need new abstractions. We propose two language abstractions, QoS type and QoS target, to capture two fundamental aspects of user QoS experience. We then present GreenWeb, a set of language extensions that empower developers to easily express the QoS abstractions as program annotations. As a proof of concept, we develop a GreenWeb runtime, which intelligently determines how to deliver specified user QoS expectation while minimizing energy consumption. Overall, GreenWeb shows significant energy savings (29.2% ∼ 66.0%) over Android’s default Interactive governor with few QoS violations. Our work demonstrates a promising first step toward language innovations for energy-efficient Web computing.
- “9 Causes of Bad App Reviews.” http://blog.monkop.com/ post/120657007496/9-causes-of-bad-app-reviewsGoogle Scholar
- “Android CPUFreq Governors.” https://android.googlesource. com/kernel/common/+/android-4.4/Documentation/cpu-freq/ governors.txtGoogle Scholar
- “Android WebView APIs.” http://developer.android.com/ reference/android/webkit/WebView.htmlGoogle Scholar
- “big.LITTLE Technology: The Future of Mobile.” https://www.arm.com/files/pdf/big LITTLE Technology the Futue of Mobile.pdfGoogle Scholar
- “CSS animation Event.” https://developer.mozilla.org/en-US/ docs/Web/Events/animationendGoogle Scholar
- “CSS Animations.” http://www.w3.org/TR/css3-animations/Google Scholar
- “CSS Pseudo-classes.” http://www.w3.org/TR/selectors/ #pseudo-classesGoogle Scholar
- “CSS transitionend Event.” https://developer.mozilla.org/ en-US/docs/Web/Events/transitionendGoogle Scholar
- “CSS Transitions.” http://www.w3.org/TR/css3-transitions/Google Scholar
- “CSS Will Change Module Level 1.” http://www.w3.org/TR/ css-will-change-1/Google Scholar
- “CSS3 Media Queries.” http://www.w3.org/TR/ css3-mediaqueries/Google Scholar
- “Document Object Model (DOM).” http://www.w3.org/ DOM/Google Scholar
- “HTTrack.” https://www.httrack.com/Google Scholar
- “iOS Developer Library: UIWebView.” https: //developer.apple.com/library/ios/documentation/UIKit/ Reference/UIWebView Class/Google Scholar
- “Jank Busting for Better Rendering Performance.” http://www.html5rocks.com/en/tutorials/speed/rendering/Google Scholar
- “NVidia: Adaptive VSync Technology.” http://www.geforce. com/hardware/technology/adaptive-vsync/technologyGoogle Scholar
- “Speed, Performance, and Human Perception.” http://chimera.labs.oreilly.com/books/1230000000545/ch10. html#SPEED PERFORMANCE HUMAN PERCEPTIONGoogle Scholar
- “Survey: Exploring the Reasons Users Complain about Apps.” http://www.fiercedeveloper.com/story/ survey-exploring-reasons-users-complain-about-apps/ 2012-11-09Google Scholar
- “The Evolution of HTML5.” http://www.instantshift.com/ 2012/07/20/the-evolution-of-html5-infographic/Google Scholar
- “The Evolution of the Web.” http://www.evolutionoftheweb. com/Google Scholar
- “Timing Control for Script-based Animations.” http: //www.w3.org/TR/animation-timing/Google Scholar
- “V-sync.” https://en.wikipedia.org/wiki/Screen tearing# V-syncGoogle Scholar
- “Your Favourite App isnt Native.” http://kennethormandy. com/journal/your-favourite-app-isnt-nativeGoogle Scholar
- “Nvidia Tegra 4 Family CPU Architecture: 4-PLUS-1 Quad core,” in Nvidia Whitepaper, 2013.Google Scholar
- “Android Fragmentation Visualized,” 2014. http://opensignal. com/assets/pdf/reports/2014 08 fragmentation report.pdfGoogle Scholar
- “Chromium browser,” 2015. http://www.chromium.org/HomeGoogle Scholar
- Alexa, “Alexa,” 2015. http://www.alexa.com/Google Scholar
- W. Baek and T. M. Chilimbi, “Green: a Framework for Supporting Energy-conscious Programming using Controlled Approximation,” in Proc. of PLDI, 2010. Google Scholar
Digital Library
- E. A. Burton, G. Schrom, F. Paillet, J. Douglas, W. J. Lambert, K. Radhakrishnan, and M. J. Hill, “FIVR: Fully Integrated Voltage Regulators on 4th Generation Intel Core SoCs,” in Proc. of APEC, 2014.Google Scholar
- M. Butkiewicz, D. Wang, Z. Wu, H. V. Madhyastha, and V. Sekar, “Klotski: Reprioritizing Web Content to Improve User Experience on Mobile Devices,” in Proc. of NSDI, 2015. Google Scholar
Digital Library
- S. K. Card, G. G. Robertson, and J. D. Mackinlay, “The Information Visualizer: An Information Workspace,” in Proc. of CHI, 1991. Google Scholar
Digital Library
- G. Chadha, S. Mahlke, and S. Narayanasamy, “EFetch: Optimizing Instruction Fetch for Event-driven Web Applications,” in Proc. of PACT, 2014. Google Scholar
Digital Library
- ——, “Accelerating Asynchronous Programs through Event Sneak Peek,” in Proc. of ISCA, 2015. Google Scholar
Digital Library
- M. Claypool, K. Claypool, and F. Damaa, “The Effects of Frame Rate and Resolution on Users Playing First Person Shooter Games,” in Proc. of Multimedia Computing and Networking, 2006.Google Scholar
Cross Ref
- M. Cohen, H. S. Zhu, S. E. Emgin, and Y. D. Liu, “Energy Types,” in Proc. of OOPSLA, 2012. Google Scholar
Digital Library
- M. Dong and L. Zhong, “Chameleon: a Color-adaptive Web Browser for Mobile OLED Displays,” in Proc. of MobiSys, 2012. Google Scholar
Digital Library
- K. Eaton, “How 1s Could Cost Amazon $1.6 Billion in Sales,” 2013. http://www.fastcompany.com/1825005/ how-one-second-could-cost-amazon-16-billion-salesGoogle Scholar
- Y. Endo, Z. Wang, J. Chen, and M. Seltzer, “Using Latency to Evaluate Interactive System Performance,” in Proc. of OSDI, 1996. Google Scholar
Digital Library
- D. Fisher and G. Saksena, “Link Prefetching in Mozilla: A Server-driven Approach,” in Web content caching and distribution. Springer, 2004, pp. 283–291. Google Scholar
Digital Library
- D. Glazkov, “User Agent Intervention.” http://bit.ly/ user-agent-interventionGoogle Scholar
- M. Halpern, Y. Zhu, R. Peri, and V. J. Reddi, “Mosaic: Crossplatform User-interaction Record and Replay for the Fragmented Android Ecosystem,” in Proc. of ISPASS, 2015.Google Scholar
Cross Ref
- M. Halpern, Y. Zhu, and V. J. Reddi, “Mobile CPU’s Rise to Power: Quantifying the Impact of Generational Mobile CPU Design Trends on Performance, Energy, and User Satisfaction,” in Proc. of HPCA, 2016.Google Scholar
Cross Ref
- S. Hao, D. Li, W. G. Halfond, and R. Govindan, “Estimating Mobile Application Energy Consumption using Program Analysis,” in Proc. of ICSE, 2013. Google Scholar
Digital Library
- S. He, Y. Liu, and H. Zhou, “Optimizing Smartphone Power Consumption through Dynamic Resolution Scaling,” in Proc. of MobiCom, 2015. Google Scholar
Digital Library
- J. Huang, F. Qian, A. Gerber, Z. M. Mao, S. Sen, and O. Spatscheck, “A Close Examination of Performance and Power Characteristics of 4G LTE Networks,” in Proc. of MobiSys, 2012. Google Scholar
Digital Library
- A. Kansal, S. Saponas, A. B. Brush, K. S. McKinley, T. Mytkowicz, and R. Ziola, “The Latency, Accuracy, and Battery (LAB) Abstraction: Programmer Productivity and Energy Efficiency for Continuous Mobile Context Sensing,” in Proc. of OOPSLA, 2013. Google Scholar
Digital Library
- W. Kim, M. S. Gupta, G.-Y. Wei, and D. Brooks, “System Level Analysis of Fast, Per-Core DVFS using On-Chip Switching Regulators,” in Proc. of HPCA, 2008.Google Scholar
- KPCB, “KPCB 2015 Internet Trends,” 2015. http: //www.kpcb.com/blog/2015-internet-trendsGoogle Scholar
- R. Kumar, K. I. Farkas, N. P. Jouppi, P. Ranganathan, and D. M. Tullsen, “Single-ISA Heterogeneous Multi-Core Architectures: The Potential for Processor Power Reduction,” in Proc. of MICRO, 2003. Google Scholar
Digital Library
- P. Lewis, “Rendering Performance,” 2014. https://developers. google.com/web/fundamentals/performance/rendering/Google Scholar
- T. Li, C. An, Z. Tian, A. T. Campbell, and X. Zhou, “Human Sensing Using Visible Light Communication,” in Proc. of MobiCom, 2015. Google Scholar
Digital Library
- M. Linares-Vásquez, G. Bavota, C. Bernal-Cárdenas, R. Oliveto, M. Di Penta, and D. Poshyvanyk, “Mining Energy-greedy API Usage Patterns in Android Apps: an Empirical Study,” in Proc. of MSR, 2014. Google Scholar
Digital Library
- D. Lo, T. Song, and G. E. Suh, “Prediction-Guided Performance-Energy Trade-off for Interactive Applications,” in Proc. of MICRO, 2015. Google Scholar
Digital Library
- R. B. Miller, “Response Time in Man-computer Conversational Transactions,” in AFIPS Fall Joint Computer Conference, 1968. Google Scholar
Digital Library
- N. C. Nachiappan, P. Yedlapalli, N. Soundararajan, A. Sivasubramaniam, M. T. Kandemir, R. Iyer, and C. R. Das, “Domain Knowledge based Energy Management in Handhelds,” in Proc. of HPCA, 2015.Google Scholar
Cross Ref
- N. C. Nachiappan, H. Zhang, J. Ryoo, N. Soundararajan, A. Sivasubramaniam, M. T. Kandemir, R. Iyer, and C. R. Das, “VIP: Virtualizing IP Chains on Handheld Platforms,” in Proc. of ISCA, 2015. Google Scholar
Digital Library
- J. Nielsen, Usability Engineering. Morgan Kaufmann, 1993. Google Scholar
Digital Library
- ODroid, “ODROID-XU+E Development Board,” 2015. http://www.hardkernel.com/main/products/prdt info. php?g code=G137463363079Google Scholar
- A. J. Oliner, A. P. Iyer, I. Stoica, E. Lagerspetz, and S. Tarkoma, “Carat: Collaborative Energy Diagnosis for Mobile Devices,” in Proc. of Sensys, 2013. Google Scholar
Digital Library
- A. Pathak, Y. C. Hu, and M. Zhang, “Where is the Energy Spent Inside My App?: Fine Grained Energy Accounting on Smartphones with Eprof,” in Proc. of EuroSys, 2012. Google Scholar
Digital Library
- M. Pradel, P. Schuh, G. Necula, and K. Sen, “EventBreak: Analyzing the Responsiveness of User Interfaces through Performance-guided Test Generation,” in Proc. of OOPSLA, 2014. Google Scholar
Digital Library
- K. Sakamoto, “Time-to-first-X-paint Metrics: Status and Refinement Plans,” 2015. https://docs.google.com/document/d/ 1Owfs6arciEnWgT2-8bWCcHdYRIKRKZ0Xj8UtqRx4c3k/ editGoogle Scholar
- A. Sampson, W. Dietl, E. Fortuna, D. Gnanapragasam, L. Ceze, and D. Grossman, “EnerJ: Approximate Data Types for Safe and General Low-Power Computation,” in Proc. of PLDI, 2011. Google Scholar
Digital Library
- C. Shepard, A. Rahmati, C. Tossell, L. Zhong, and P. Kortum, “LiveLab: Measuring Wireless Networks and Smartphone Users in the Field,” in SIGMETRICS Performance Evaluation Review, 2011. Google Scholar
Digital Library
- S. Singh, “HTML5 On The Rise: No Longer Ahead Of Its Time,” 2015. http://techcrunch.com/2015/10/28/ html5-on-the-rise-no-longer-ahead-of-its-time/Google Scholar
- J. Sorber, A. Kostadinov, M. Garber, M. Brennan, M. D. Corner, and E. D. Berger, “Eon: A Language and Runtime System for Perpetual Systems,” in Proc. of SenSys, 2007. Google Scholar
Digital Library
- M. A. Suleman, Y. N. Patt, E. Sprangle, A. Rohillah, A. Ghuloum, and D. Carmean, “Asymmetric Chip Multiprocessors: Balancing Hardware Efficiency and Programmer Efficiency,” The University of Texas as Austin, Technical Report TR-HPS- 2007-001, 2007.Google Scholar
- Q. Wu, V. Reddi, Y. Wu, J. Lee, D. Connors, D. Brooks, M. Martonosi, and D. W. Clark, “A Dynamic Compilation Framework for Controlling Microprocessor Energy and Performance,” in Proc. of MICRO, 2005. Google Scholar
Digital Library
- F. Xie, M. Martonosi, and S. Malik, “Compile-time Dynamic Voltage Scaling Settings: Opportunities and Limits,” in Proc. of PLDI, 2003. Google Scholar
Digital Library
- Y. Zhu, M. Halpern, and V. J. Reddi, “Event-based Scheduling for Energy-Efficient QoS (eQoS) in Mobile Web Applications,” in Proc. of HPCA, 2015.Google Scholar
Cross Ref
- ——, “The Role of the CPU in Energy-Efficient Mobile Web Browsing,” in Micro, IEEE, 2015.Google Scholar
- Y. Zhu and V. J. Reddi, “High-Performance and Energy-Efficient Mobile Web Browsing on Big/Little Systems,” in Proc. of HPCA, 2013. Google Scholar
Digital Library
- ——, “WebCore: Architectural Support for Mobile Web Browsing,” in Proc. of ISCA, 2014. Google Scholar
Digital Library
Index Terms
GreenWeb: language extensions for energy-efficient mobile web computing
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