Assessing mobile applications performance and energy consumption through experiments and Stochastic models

Energy consumption, execution time, and availability are common terms in discussions on application development for mobile devices. Mobile applications executing in a mobile cloud computing (MCC) environment must consider several issues, such as Internet connections problems and CPU performance. Misconceptions during the design phase can have a significant impact on costs and time-to-market, or even make the application development unfeasible. Anticipating the best configuration for each type of application is a challenge that many developers are not prepared to tackle. In this work, we propose models to rapidly estimate execution time, availability, and energy consumption of mobile applications executing in an MCC environment. We defined a methodology to create and validate Deterministic and Stochastic Petri net (DSPN) models to evaluate these three critical metrics. The DSPNs results were compared with results obtained through experiments performed on a testbed environment. We analyzed an image processing application, regarding connections type (WLAN, WiFi, and 3G), servers type (MCC or cloudlet), and functionalities performance. Our numerical analyses indicate, for instance, that the use of a cloudlet significantly improves performance and energy efficiency. Besides, the baseline scenario took us one month to implement, while modeling and evaluation the three scenarios required less than one day. In this way, our DSPN models represent a powerful tool for mobile developers to plan efficient and cost-effective mobile applications. They allow rapidly assess execution time, availability, and energy consumption metrics to improve the quality of mobile applications.