An adaptive offloading framework for Android applications in mobile edge computing  

作　　者：Xing CHEN Shihong CHEN Yun MA Bichun LIU Ying ZHANG Gang HUANG 

机构地区：[1]College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China [2]Fujian Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou 350116, China [3]School of Software, Tsinghua University, Beijing 100084, China [4]Key Laboratory of High Confidence Software Technologies, Ministry of Education, Beijing 100871, China

出　　处：《Science China(Information Sciences)》2019年第8期110-126,共17页中国科学（信息科学）（英文版）

基　　金：supported by National Key R&D Program of China (Grant No. 2017YFB1002000);National Natural Science Foundation of China (Grant No. 61725201);Talent Program of Fujian Province for Distinguished Young Scholars in Higher Education;supported by China Postdoctoral Science Foundation

摘　　要：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.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.

关 键 词：computation OFFLOADING software adaptation MOBILE EDGE COMPUTING application REFACTORING ANDROID 

分 类 号：TP[自动化与计算机技术]