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Assessing mobile applications performance and energy consumption through experiments and Stochastic models
Computing ( IF 3.7 ) Pub Date : 2019-01-24 , DOI: 10.1007/s00607-019-00707-6
Júlio Mendonça , Ermeson Andrade , Ricardo Lima

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.

中文翻译:

通过实验和随机模型评估移动应用程序的性能和能耗

能耗、执行时间和可用性是移动设备应用程序开发讨论中的常用术语。在移动云计算 (MCC) 环境中执行的移动应用程序必须考虑几个问题,例如 Internet 连接问题和 CPU 性能。设计阶段的误解会对成本和上市时间产生重大影响,甚至会使应用程序开发不可行。为每种类型的应用程序预测最佳配置是一项挑战,许多开发人员还没有准备好应对。在这项工作中,我们提出了一些模型来快速估计在 MCC 环境中执行的移动应用程序的执行时间、可用性和能耗。我们定义了一种方法来创建和验证确定性和随机 Petri 网 (DSPN) 模型,以评估这三个关键指标。将DSPNs 结果与通过在测试平台环境中进行的实验获得的结果进行比较。我们分析了一个图像处理应用程序,涉及连接类型(WLAN、WiFi 和 3G)、服务器类型(MCC 或 cloudlet)和功能性能。例如,我们的数值分析表明,使用小云可以显着提高性能和能源效率。此外,基线场景需要我们一个月的时间来实现,而三个场景的建模和评估只需要不到一天的时间。通过这种方式,我们的 DSPN 模型代表了移动开发人员规划高效且具有成本效益的移动应用程序的强大工具。
更新日期:2019-01-24
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