Optimizing mobile application performance with model-driven engineering

22Citations
Citations of this article
75Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Future embedded and ubiquitous computing systems will operate continuously on mobile devices, such as smartphones, with limited processing capabilities, memory, and power. A critical aspect of developing future applications for mobile devices will be ensuring that the application provides sufficient performance while maximizing battery life. Determining how a software architecture will affect power consumption is hard because the impact of software design on power consumption is not well understood. Typically, the power consumption of a mobile software architecture can only be determined after the architecture is implemented, which is late in the development cycle when design changes are costly. Model-driven Engineering (MDE) is a promising solution to this problem. In an MDE process, a model of the software architecture can be built and analyzed early in the design cycle to identify key characteristics, such as power consumption. This paper describes current research in developing an MDE tool for modeling mobile software architectures and using them to generate synthetic emulation code to estimate power consumption properties. The paper provides the following contributions to the study of mobile software development: (1) it shows how models of a mobile software architecture can be built, (2) it describes how instrumented emulation code can be generated to run on the target mobile device, and (3) it discusses how this emulation code can be used to glean important estimates of software power consumption and performance. © IFIP International Federation for Information Processing 2009.

Cite

CITATION STYLE

APA

Thompson, C., White, J., Dougherty, B., & Schmidt, D. C. (2009). Optimizing mobile application performance with model-driven engineering. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5860 LNCS, pp. 36–46). https://doi.org/10.1007/978-3-642-10265-3_4

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free