GreenWeb: Language Extensions for Energy-Efficient Mobile Web Computing
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 effi- ciency. 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.
Wed 15 JunDisplayed time zone: Tijuana, Baja California change
13:30 - 15:00 | Energy & PerformanceResearch Papers at Grand Ballroom San Rafael Chair(s): Manuel Hermenegildo IMDEA Software Institute and T.U. of Madrid (UPM) | ||
13:30 30mTalk | Effective Padding of Multi-Dimensional Arrays to Avoid Cache Conflict Misses Research Papers Changwan Hong , Wenlei Bao , Albert Cohen INRIA, Sriram Krishnamoorthy Pacific Northwest National Laboratories, Louis-Noël Pouchet Ohio State University, J. Ramanujam Louisiana State University, Fabrice Rastello INRIA, France, P. Sadayappan Ohio State University Media Attached | ||
14:00 30mTalk | GreenWeb: Language Extensions for Energy-Efficient Mobile Web Computing Research Papers Link to publication Media Attached | ||
14:30 30mTalk | Input Responsiveness: Using Canary Inputs to Dynamically Steer Approximation Research Papers Michael A. Laurenzano University of Michigan, Parker Hill , Mehrzad Samadi University of Michigan, Scott Mahlke University of Michigan, Jason Mars University of Michigan, Lingjia Tang University of Michigan Media Attached |