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Users and Batteries : Interactions and Adaptive Power Management in Mobile Systems

Users and Batteries : Interactions and Adaptive Power Management in Mobile Systems. Nilanjan Banerjee 1 , Ahmad Rahmati 2 , Mark Corner 1 , Sami Rollins 3 , Lin Zhong 2. 1 University of Massachusetts, Amherst. 2 Rice University. 3 University of San Francisco.

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Users and Batteries : Interactions and Adaptive Power Management in Mobile Systems

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  1. Users and Batteries : Interactions and Adaptive Power Management in Mobile Systems Nilanjan Banerjee1, Ahmad Rahmati2, Mark Corner1, Sami Rollins3, Lin Zhong2 1University of Massachusetts, Amherst 2 Rice University 3University of San Francisco http://prisms.cs.umass.edu/llama

  2. Scenario: why did my laptop switch of ? • You are riding a bus to work and you are five minutes away • you are working on your laptop finishing a presentation • Suddenly your laptop turns of ! Grrr … !!! • your laptop battery was running low • You would have charged your laptop within 5 minutes anyway • you could have completed your presentation

  3. Scenario : working on an airplane • You are working on your presentation on a flight to Austria • Midway through your flight your laptop turns of • your battery could only last for three hours • Wish your laptop adapted to your charging behavior !

  4. Problem : power management Vs user • Power management for mobile systems are not user-centric • do not adapt to changing user behavior and device modalities • No understanding of how users use energy of their mobile device • assumption: users desire maximum lifetime out of batteries User Battery

  5. Solution: energy for the user energy management user behavior • Understand user-battery interaction in mobile systems • when, why and where do users recharge • Built user-centric power management policy for mobile systems • policy which adapts to varying user-battery behavior

  6. Outline • User-study on laptops and mobile phone • research methods for user-study • Insights from the user study • when, where, and why do users recharge batteries • how predictable are recharge patterns • User-centric power management • design and implementation, and evaluation of Llama • Related work • Conclusions

  7. Study of user-battery interaction • Goal : examine where, when, and why people recharge • subjects recruited from friends, family, mailing lists • used three complimentary research methods In-situ survey Trace Collection User Interviews 56 Laptops 15-150 days 10 Mobile phones 42-77 days 10 Laptops 10 Mobile phone age 20-26 years 10 Laptop 415 response 10 Mobile phone 91 responses

  8. Trace collection • Goal : collect quantitative records of battery level • Laptop implementation is Java based • runs on Microsoft Windows and Apple OS X • records measurements periodically • uploads data automatically to a central server once a day • Mobile phone tool is written in C++ • runs on Microsoft Windows Mobile • tool distributed pre-installed on T-Mobile MDA phones • aggressive : wakes the phone very minute to take reading

  9. User interviews • Gather qualitative data regarding user-battery interaction • understand context of recharge • Provided sample scenarios to participants to think about • last time the user was faced with a low battery condition ? • what impact did it have on their future behavior ? • Questions about when, why, and where users recharge ? • Encouraged users to tell their stories and anecdotes

  10. In-situ pop-up survey • Filtered out intervals of less than 5 minutes between recharges Goal: In-situ information about why users recharge Laptop Mobile Phone Disappears after a minute

  11. Outline • User-study on laptops and mobile phone • research methods for user-study • Insights from the user study • when, where, and why do users recharge batteries • how predictable are recharge patterns • User-centric power management • design and implementation, and evaluation of Llama • Related work • Conclusions

  12. Users have energy to spare Laptops 50% of the recharges occur when the battery is half full Fraction of users use their laptops like desktops

  13. Users have energy to spare Mobile Phones 60% of the recharges occur when the battery is half full Most recharges occur between 25-75 %

  14. Recharges are context driven Limited Opportunities Ahead Limited Opportunities Ahead System Reminder System Reminder Low Battery Low Battery Convenient location Convenient Time Convenient location Convenient Time Mobile Phones Laptops Fraction of recharges are driven by context Low battery corresponded to 40% of the battery remaining

  15. Variations across users and devices Laptops Mobile Phones Variation in recharge pattern across mobile phones and laptops Variation across recharge patterns across users

  16. Summary of the user-study • Recharges occur with significant energy remaining in batteries • Charging is mostly driven by context and battery levels • Users and devices show significant variation in battery usage • power management should adapt with users and devices I usually charge in the office when the indicator shows 1 bar I always recharge every night

  17. User-centric power management • Users charge their system with significant battery left • accurately predict excess energy left in the battery • proactively use the remaining energy to improve QoS • Optimization framework for power management • maximize the excess energy usable by applications • minimize the probability of running out of battery • try to avoid true low battery levels

  18. Llama : design and implementation • Example Scenario • Confidence of not exceeding battery capacity = 0.95 • Llama determines present battery percentage (Cp) = 30% • creates a histogram of recharges below Cp (H) • Llama calculates 95% of the time user recharges by 10% • devote 10% to Llama application

  19. Llama applications and deployment Screen Brightness excess energy to adjust screen brightness Web prefetching prefetching a random webpage download interval determines aggressiveness Health monitoring reports preprogrammed data upload interval determines aggressiveness

  20. Llama deployment demographics

  21. Llama evaluation Laptops Mobile Phones Llama used energy depending on battery left at recharge Beneficial use of Llama more web data, and brighter display

  22. Post-Llama recharge behavior

  23. Feedback loop with user Recharge cycle becomes shorter and shorter, frustrating the user Plan to address the problem in future versions of Llama

  24. Post-Llama user study • Interviews to evaluate negative effects of Llama • impact of Llama on battery lifetime • All mobile phone users but one showed similar satisfaction • “The battery lifetime was better last month, I have to recharge it every day now, but it used to be every day and a half” Laptop user Even though I didn’t notice it, I would definitely care in situations where I require maximum battery life It must have been small, since I didn’t notice it

  25. Future work • Evaluate the positive effects of Llama • what are the user-perceived benefits of Llama ? • Improve the prediction algorithm of Llama • use contextual information such as location, work patterns • Experiment on different mobile devices like music players • less biased or demographically weighted subject selection

  26. Related work • MyExperience in-situ survey tool [Mobisys 2007] • tool for in-situ profiling and survey • Human factor in energy management • user-interface design on energy efficiency [Vallero et al.] • visual perception to reduce energy of LCDs [Chen et al.] • Tools for studying mobile users in natural settings • logging tool for studying HCI [Demumieux et al.] • Balance performance and system-wide energy consumption • Odyssey [Flinn et al.], Ecosystem [Zeng et al.]

  27. Conclusions • First glimpse of user-battery interaction • traces would be available through the traces.cs project • User study produced three key observations • users leave excess energy in the battery on recharge • charging behavior is driven by opportunity and context • significant variations across users and systems • Built an user-centric energy management system called Llama • it can scale energy usage to user behavior

  28. Users and Batteries : Interactions and Adaptive Power Management in Mobile Systems Nilanjan Banerjee1, Ahmad Rahmati2, Mark Corner1, Sami Rollins3, Lin Zhong2 1University of Massachusetts, Amherst 2 Rice University 3University of San Francisco http://prisms.cs.umass.edu/llama

  29. HotMobile 2008 Napa, CA, February 25-26, 2008Submissions: October 16, 2007

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