TY - GEN
T1 - Usage pattern-based prefetching
T2 - 12th International Conference on Computational Science and Its Applications, ICCSA 2012
AU - Song, Hokwon
AU - Min, Changwoo
AU - Kim, Jeehong
AU - Eom, Young Ik
PY - 2012
Y1 - 2012
N2 - The startup time of applications is very important as a user perspective performance. If page faults occur frequently in the startup time, the user experience is subjected to an adverse effect. To reduce page faults, the prefetching scheme is used in the traditional OS. Previous studies proposed various schemes, but the most research was conducted for desktop PCs or special embedded devices. We propose the usage pattern-based prefetching scheme which is suitable to mobile devices. Therefore, this paper focuses on the user's applications usage patterns and the improvement of the startup time of application on mobile devices. To inspect the usage patterns, we collect the dataset of the application usage and then analyze collected data. Additionally, considering mobile devices which have relatively poor hardware resources, the lightweight prediction model is employed in the new scheme. The proposed scheme is implemented on both Android 2.2 and Linux kernel 2.6.29. It is tested on the emulator and evaluated by using the dataset. The startup time is improved about 5%, and the accuracy of the prediction is shown up to 59% for the practical dataset.
AB - The startup time of applications is very important as a user perspective performance. If page faults occur frequently in the startup time, the user experience is subjected to an adverse effect. To reduce page faults, the prefetching scheme is used in the traditional OS. Previous studies proposed various schemes, but the most research was conducted for desktop PCs or special embedded devices. We propose the usage pattern-based prefetching scheme which is suitable to mobile devices. Therefore, this paper focuses on the user's applications usage patterns and the improvement of the startup time of application on mobile devices. To inspect the usage patterns, we collect the dataset of the application usage and then analyze collected data. Additionally, considering mobile devices which have relatively poor hardware resources, the lightweight prediction model is employed in the new scheme. The proposed scheme is implemented on both Android 2.2 and Linux kernel 2.6.29. It is tested on the emulator and evaluated by using the dataset. The startup time is improved about 5%, and the accuracy of the prediction is shown up to 59% for the practical dataset.
KW - Mobile device
KW - Prefetching
KW - Usage pattern
UR - https://www.scopus.com/pages/publications/84863922488
U2 - 10.1007/978-3-642-31137-6_17
DO - 10.1007/978-3-642-31137-6_17
M3 - Conference contribution
AN - SCOPUS:84863922488
SN - 9783642311369
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 227
EP - 237
BT - Computational Science and Its Applications - 12th International Conference, ICCSA 2012, Proceedings
Y2 - 18 June 2012 through 21 June 2012
ER -