1 University of Massachusetts, Amherst 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 2 Rice University 3 University of San Francisco

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

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 !

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 Battery User

Solution: energy for the user 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 user behavior energy management

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

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 10 Laptops 10 Mobile phone age years 10 Laptop 415 response 10 Mobile phone 91 responses 56 Laptops days 10 Mobile phones days Trace CollectionUser Interviews In-situ survey

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

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

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

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

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

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

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

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

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 always recharge every night I usually charge in the office when the indicator shows 1 bar

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

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

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

Llama deployment demographics ParticularsLaptopMobile Phone ApplicationScreen brightness Web prefetching Health monitoring Subjects2 females, 8 male years 1 female, 9 males years Number of Days30

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

Post-Llama recharge behavior ParticularsLaptopMobile Phone Number of recharges (per week) Pre-Llama = 6.5 Post-Llama = 7.8 Pre-Llama = 10.1 Post-Llama = 8.9 Recharges below 5%Pre-Llama = 1% Post-Llama = 1% Pre-Llama = 4% Post-Llama = 7%

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

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” It must have been small, since I didn’t notice it Even though I didn’t notice it, I would definitely care in situations where I require maximum battery life Laptop user

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

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.]

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

1 University of Massachusetts, Amherst 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 2 Rice University 3 University of San Francisco

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