Interactive wearable devices bring dramatic changes to apps and hardware, leaving the operating system (OS) design in the mist. To this end, we thoroughly examine the execution efficiency of Android Wear, a popular wearable OS.
This work is done by XSEL at the Purdue ECE.
[MobiSys’17] "Characterizing Smartwatch Usage in The Wild," Xing Liu, Tianyu Chen, Feng Qian, Zhixiu Guo, Felix Xiaozhu Lin, Xiaofeng Wang, and Kai Chen, in Proc. ACM Int. Conf. Mobile Systems, Applications and Services, 2017.
[HotMobile’17] "Decelerating Suspend and Resume in OS," Shuang Zhai, Liwei Guo, Xiangyu Li, and Felix Xiaozhu Lin, in Proc. ACM Int. Workshop on Mobile Computing Systems and Applications, 2017.
[MobiSys’16] "Understanding the Characteristics of Android Wear OS," Renju Liu and Felix Xiaozhu Lin, in Proc. ACM Int. Conf. Mobile Systems, Applications and Services, 2016.
[ApSys’15] "Anatomizing System Activities on Interactive Wearable Devices," Renju Liu, Lintong Jiang, Ningzhe Jiang, and Felix Xiaozhu Lin, in Proc. ACM Asia-Pacific Workshop on Systems, 2015.
Download the important tools/data here. They include:
Raw trace data: the raw trace data contains the data used to generate each figure/table in the paper.
Parsers: all the important parsing tools such as finding the interested time interval for the events.
A customized kernel: the kernel has been modified from lenok 3.10 (upstream) so that it can support our atrace tool (enabled suspend/resume category of ftrace) and provide monolithic time clock for tracing purpose.
This work was supported in part by NSF Award CNS-1464157 and a Google Faculty Award.
We thank companies such as Google and Qualcomm for making their software free and/or opensource.
All the pre-existing code and data are subject to their own licenses. All the userspace programs and kernel modules resulted from this project are under FreeBSD license.
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