SCIENCE CHINA Information Sciences, Volume 62, Issue 8: 082102(2019) https://doi.org/10.1007/s11432-018-9749-8

An adaptive offloading framework for Android applications in mobile edge computing

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  • ReceivedMar 8, 2018
  • AcceptedDec 19, 2018
  • PublishedJul 12, 2019


Mobile edge computing (MEC) provides a fresh opportunity to significantly reduce the latency and battery energy consumption of mobile applications. It does so by enabling the offloading of parts of the applications on mobile edges, which are located in close proximity to the mobile devices. Owing to the geographical distribution of mobile edges and the mobility of mobile devices, the runtime environment of MEC is highly complex and dynamic. As a result, it is challenging for application developers to support computation offloading in MEC compared with the traditional approach in mobile cloud computing, where applications use only the cloud for offloading. On the one hand, developers have to make the offloading adaptive to the changing environment, where the offloading should dynamically occur among available computation nodes. On the other hand, developers have to effectively determine the offloading scheme each time the environment changes. To address these challenges, this paper proposes an adaptive framework that supports mobile applications with offloading capabilities in MEC. First, based on our previous study (DPartner), a new design pattern is proposed to enable an application to be dynamically offloaded among mobile devices, mobile edges, and the cloud. Second, an estimation model is designed to automatically determine the offloading scheme. In this model, different parts of the application may be executed on different computation nodes. Finally, an adaptive offloading framework is implemented to support the design pattern and the estimation model. We evaluate our framework on two real-world applications. The results demonstrate that our approach can aid in reducing the response time by 8%–50% and energy consumption by 9%–51% for computation-intensive applications.


This work was supported by National Key RD Program of China (Grant No. 2017YFB1002000), National Natural Science Foundation of China (Grant No. 61725201), and Talent Program of Fujian Province for Distinguished Young Scholars in Higher Education. Yun MA's work was supported by China Postdoctoral Science Foundation.


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