Reducing Launch Time of Apps by Context-Aware Process Management

碩士 === 國立清華大學 === 資訊工程學系 === 101 === With the burgeoning market of smart phones, user experience is becoming a dominating factor affecting how people buy a smart phone. Though user experience may include a lot of aspects, the launch time of applications definitely influences the perception of the us...

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Bibliographic Details
Main Authors: Lin, Yen-Chen, 林彥成
Other Authors: King, Chung-Ta
Format: Others
Language:en_US
Published: 2013
Online Access:http://ndltd.ncl.edu.tw/handle/67442255475209985437
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Summary:碩士 === 國立清華大學 === 資訊工程學系 === 101 === With the burgeoning market of smart phones, user experience is becoming a dominating factor affecting how people buy a smart phone. Though user experience may include a lot of aspects, the launch time of applications definitely influences the perception of the user about the responsiveness of the phone, and thus becomes a significant element of user experience. Android uses a mechanism, Activity Stack, to handle the launch of applications. If an application is launched from the activity stack, the launch time can be shorter. Thus, it is important to keep the applications in the activity stack when they are to be launched. However, applications are loaded into the activity stack on-demand and are removed using the LRU strategy. It turns out that, when an application is "demanded", it is very likely not in the activity stack, leading to a long launch time. Ideally, if we can predict what applications a user will use in the near future and make sure they are loaded and kept in the activity stack, then the application launch time may be reduced. In this thesis, we use a context-aware strategy to predict the future use of applications. We have modified the Android framework to pre-launch an application if it is predicted hot but not in the activity stack. We have also replaced the LRU policy in Android so that a predicted hot application will not be removed from the activity stack. The proposed context-aware prediction method is evaluated based on the activity traces collected from five real users over a three-week period. The results show that our method allows in average 27% more applications to be launched from the activity stack when compared with the original Android system.