Extending Mobile Computer Battery Life through Energy-Aware Adaptation Jason Flinn CMU-CS-01-171 December 2001 School of Computer Science Computer Science Department Carnegie Mellon University Pittsburgh, PA Thesis Committee: M. Satyanarayanan, Chair Todd Mowry Dan Siewiorek Keith Farkas, Compaq Western Research Laboratory Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy. Copyright c 2001 Jason Flinn This research was supported by the Defense Advanced Research Projects Agency (DARPA) and the Air Force Materiel Command (AFMC) under contracts F19628-93-C-0193 and F19628-96-C-0061, DARPA, the Space and Naval Warfare Systems Center (SPAWAR) / U.S. Navy (USN) under contract N660019928918, the National Science Foundation (NSF) under contracts CCR-9901696 and ANI-0081396, IBM Corporation, Intel Corporation, AT&T, Compaq, Hughes, and Nokia. The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies or endorsements, either express or implied, of DARPA, AFMC, SPAWAR, USN, the NSF, IBM, Intel, AT&T, Compaq, Hughes, Nokia, Carnegie Mellon University, the U.S. Govern- ment, or any other entity. Keywords: Energy-aware adaptation, application-aware adaptation, power manage- ment, mobile computing, ubiquitous computing, remote execution Abstract Energy management has been a critical problem since the earliest days of mobile com- puting. The amount of work one can perform while mobile is fundamentally constrained by the limited energy supplied by one’s battery. Although a large research investment in low-power circuit design and hardware power management has led to more energy-efficient systems, there is a growing realization that more is needed—the higher levels of the system, the operating system and applications, must also contribute to energy conservation. This dissertation puts forth the claim that energy-aware adaptation, the dynamic bal- ancing of application quality and energy conservation, is an essential part of a complete en- ergy management strategy. Energy-aware applications identify possible tradeoffs between energy use and application quality, but defer decisions about which tradeoffs to make un- til runtime. The operating system uses additional information available during execution, such as resource supply and demand, to advise applications which tradeoffs are best. This dissertation first shows how one can measure the energy impact of the higher levels of the system. It describes the design and implementation of PowerScope, an energy profiling tool that maps energy consumption to specific code components. PowerScope helps developers increase the energy-efficiency of their software by focusing attention on those processes and procedures that are responsible for the bulk of energy use. PowerScope is used to perform a detailed study of energy-aware adaptation, focusing on two dimensions: reduction of data and computation quality, and relocation of execu- tion to remote machines. The results of the study show that applications can significantly extend the battery lifetimes of the systems on which they execute by modifying their behav- ior. On some platforms, quality reduction and remote execution can decrease application energy usage by up to 94%. Further, the study results show that energy-aware adaptation is complementary to existing hardware energy-management techniques. The operating system can best support energy-aware applications by using goal-directed adaptation, a feedback technique in which the system monitors energy supply and demand to select the best tradeoffs between quality and energy conservation. Users specify a de- sired battery lifetime, and the system triggers applications to modify their behavior in order to ensure that the specified goal is met. Results show that goal-directed adaptation can effectively meet battery lifetime goals that vary by as much as 30%. iii iv Acknowledgments I may perhaps be unusual in that I found the writing of my dissertation to be a very enjoy- able task. I think that this is in large part due to the tremendous people with whom I have worked during this project. My advisor, Satya, made invaluable contributions not just to this dissertation, but also to my development as a research professional. He exhibited a constant optimism in the ul- timate usefulness and success of my research that helped to sustain me as I worked through the hard problems. He was also remarkably successful in keeping an eye on the “big pic- ture” and preventing me from getting mired in the details. Perhaps his most valuable con- tribution, though, lies in teaching me how to express my ideas through both the spoken and written word. I am indebted to the other members of my thesis committee, Keith Farkas, Todd Mowry, and Dan Siewiorek, for helping me select interesting research directions to explore. Their input led to the development of Spectra, which turned out to be one of the more fun projects in the dissertation. I have built upon the work of the many members of the Odyssey group. Brian Noble not only laid the foundation for this work by developing Odyssey; he also served as a role model during my first years at CMU. Dushyanth Narayanan, Eric Tilton, and Kip Walker were my brothers-in-arms in the coding trenches—we spent many late nights running experiments, getting demos to run, and building a working system. The newer students in our group, Rajesh Balan and SoYoung Park, have helped me refine my ideas and evaluate my work. It is always a pleasure when one can work with such a talented group of people. My former 8208 officemates, Hugo Patterson, David Petrou, and Sanjay Rao, served as sounding-boards for many crazy ideas. Bob Baron and Jan Harkes provided a tremendous amount of technical advice about kernel internals and the Coda file system. Eyal de Lara provided a great deal of help with the Puppeteer system. I’d like to especially thank my father for encouraging me to go back to graduate school, and my mother for reminding me of the importance of having fun. My friends from Penn, Ben Matelson, John Mayne, Patrick O’Donnell, Ted Restelli, and Geoff Taubman, have provided a support network that has withstood the test of time. I’d also like to thank the many new friends I have made here at CMU, including, but not limited to: Rajesh Balan, Angela and Joe Brown, Chris Colohan, Charlie Garrod, Chris Palmer, Carrie Sparks, Greg Steffan, Kip Walker, and Ted Wong. v vi Contents 1 Introduction 1 1.1 Energy management in mobile computing . 1 1.2 Energy-aware adaptation . 2 1.3 The thesis . 3 1.4 Road map for the dissertation . 3 2 Background 5 2.1 Energy metrics . 5 2.2 Hardware platform characteristics . 6 2.2.1 The IBM 560X laptop computer . 7 2.2.2 The Itsy pocket computer . 8 2.2.3 Comparison of platform characteristics . 9 2.3 The Odyssey platform for mobile computing . 10 2.4 Summary . 13 3 PowerScope: Profiling application energy usage 15 3.1 Design considerations . 16 3.2 Implementation . 17 3.2.1 Overview . 17 3.2.2 The System Monitor . 18 3.2.3 The Energy Monitor . 19 3.2.4 The Energy Analyzer . 21 3.3 Validation . 23 3.3.1 Accuracy . 23 3.3.2 Overhead . 26 3.4 Summary . 29 4 Energy-aware adaptation 31 4.1 Goals of the study . 31 4.2 Methodology . 32 4.3 Experimental setup . 33 4.4 Video player . 34 4.4.1 Description . 34 4.4.2 Results . 34 vii viii CONTENTS 4.5 Speech recognizer . 37 4.5.1 Description . 37 4.5.2 Results . 39 4.5.3 Results for Itsy v1.5 . 40 4.6 Map viewer . 43 4.6.1 Description . 43 4.6.2 Results . 43 4.7 Web browser . 47 4.7.1 Description . 47 4.7.2 Results . 48 4.8 Effect of concurrency . 50 4.9 Summary . 53 5 A proxy approach for closed-source environments 57 5.1 Overview . 57 5.2 Puppeteer . 58 5.3 Measurement methodology . 59 5.4 Benefits of PowerPoint adaptation . 60 5.4.1 Loading presentations . 60 5.4.2 Editing presentations . 63 5.4.3 Background activities . 65 5.4.4 Autosave . 66 5.5 Summary . 67 6 System support for energy-aware adaptation 69 6.1 Goal-directed adaptation . 69 6.1.1 Design considerations . 70 6.1.2 Implementation . 70 6.1.3 Basic validation . 74 6.1.4 Sensitivity to half-life . 78 6.1.5 Validation with longer duration experiments . 78 6.1.6 Overhead . 79 6.2 Use of application resource history . 80 6.2.1 Benefits of application resource history . 81 6.2.2 Recording application resource history . 81 6.2.3 Learning from application resource history . 84 6.2.4 Using application resource history to evaluate utility . 84 6.2.5 Using application resource history to improve agility . 88 6.2.6 Validation . 92 6.3 Summary . 98 CONTENTS ix 7 Remote execution 99 7.1 Target environment . 99 7.2 Design considerations . 100 7.2.1 Competing goals for functionality placement . 101 7.2.2 Variation in resource availability . 101 7.2.3 Self-tuning operation . 101 7.2.4 Modification to application source code . 102 7.2.5 Granularity of remote execution . 103 7.2.6 Support for remote file access . 103 7.3 Implementation . 104 7.3.1 Overview . 104 7.3.2 Application interface . 105 7.3.3 Architecture . 106 7.3.4 Resource monitors . 108 7.3.5 Predicting resource demand . 111 7.3.6 Ensuring data consistency . 112 7.3.7 Selecting the best option . 113 7.3.8 Applications . 114 7.4 Validation . 116 7.4.1 Speech recognition . 116.