Characterizing and modeling user activity on smartphones: summary
Proceedings of the ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Proceedings of the 8th international conference on Mobile systems, applications, and services
CODES/ISSS '10 Proceedings of the eighth IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis
FAST: quick application launch on solid-state drives
FAST'11 Proceedings of the 9th USENIX conference on File and stroage technologies
Smartphone usage in the wild: a large-scale analysis of applications and context
ICMI '11 Proceedings of the 13th international conference on multimodal interfaces
| Hi-index | 0.00 |
As a highly personalized computing device, smartphones present a unique new opportunity for system optimization. For example, it is widely observed that a smartphone user exhibits very regular application usage patterns (although different users are quite different in their usage patterns). User-specific high-level app usage information, when properly managed, can provide valuable hints for optimizing various system design requirements. In this article, we describe the design and implementation of a personalized optimization framework for the Android platform that takes advantage of user's application usage patterns in optimizing the performance of the Android platform. Our optimization framework consists of two main components, the application usage modeling module and the usage model-based optimization module. We have developed two novel application usage models that correctly capture typical smartphone user's application usage patterns. Based on the application usage models, we have implemented an app-launching experience optimization technique which tries to minimize user-perceived delays, extra energy consumption, and state loss when a user launches apps. Our experimental results on the Nexus S Android reference phones show that our proposed optimization technique can avoid unnecessary application restarts by up to 78.4% over the default LRU-based policy of the Android platform.