TY - GEN
T1 - Understanding the impact of number of CPU cores on user satisfaction in smartphones
AU - Poyraz, Emirhan
AU - Kashinkunti, Prethvi
AU - Schuchhardt, Matt
AU - Kishinevsky, Michael
AU - Soundararajan, Niranjan
AU - Memik, Gokhan
N1 - Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/11/12
Y1 - 2019/11/12
N2 - Understanding user experience/satisfaction with mobile systems in order to manage computational resources has become a popular approach in recent years. One of the key challenges in this area is how to gauge user satisfaction. In this paper, we study the impact of CPU configuration on user satisfaction and power consumption with real users. Specifically, we propose a system to save energy by altering active CPU core count and frequency while keeping users satisfied. The system utilizes user-facing metrics such as frame rate and input lag to predict user satisfaction and then configure CPU core count and frequency in real-time to maximize satisfaction while minimizing power consumption. We first study a set of applications in-the-lab and show that we can accurately model satisfaction with the collected user-facing metrics. We then go into-the-wild in order to evaluate the proposed system in real environments. In the wild, we build a user-independent (user-oblivious) and user-dependent (personal) model. Users test the two models and the default scheme for one-week duration, which composes 140 days of worth of data. When compared to default scheme, our results show that, without impacting satisfaction, user-independent and user-dependent models save 12.3% and 11.8% of total system energy on average, respectively.
AB - Understanding user experience/satisfaction with mobile systems in order to manage computational resources has become a popular approach in recent years. One of the key challenges in this area is how to gauge user satisfaction. In this paper, we study the impact of CPU configuration on user satisfaction and power consumption with real users. Specifically, we propose a system to save energy by altering active CPU core count and frequency while keeping users satisfied. The system utilizes user-facing metrics such as frame rate and input lag to predict user satisfaction and then configure CPU core count and frequency in real-time to maximize satisfaction while minimizing power consumption. We first study a set of applications in-the-lab and show that we can accurately model satisfaction with the collected user-facing metrics. We then go into-the-wild in order to evaluate the proposed system in real environments. In the wild, we build a user-independent (user-oblivious) and user-dependent (personal) model. Users test the two models and the default scheme for one-week duration, which composes 140 days of worth of data. When compared to default scheme, our results show that, without impacting satisfaction, user-independent and user-dependent models save 12.3% and 11.8% of total system energy on average, respectively.
KW - Energy optimization
KW - Satisfaction prediction
KW - Smartphones
KW - User satisfaction
UR - http://www.scopus.com/inward/record.url?scp=85079891123&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85079891123&partnerID=8YFLogxK
U2 - 10.1145/3360774.3360816
DO - 10.1145/3360774.3360816
M3 - Conference contribution
AN - SCOPUS:85079891123
T3 - ACM International Conference Proceeding Series
SP - 288
EP - 297
BT - Proceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems
PB - Association for Computing Machinery
T2 - 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MobiQuitous 2019
Y2 - 12 November 2019 through 14 November 2019
ER -