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Refactoring Android Java Code for On-Demand Computation Offloading

Abstract : Computation offloading is a promising way to improve the performance as well as reduce the battery energy consumption of a smartphone application by executing some part of the application on a remote server. Supporting such capability is not easy to smartphone app developers for 1) correctness: some codes, e.g. those for GPS, gravity and other sensors, can only run on the smartphone so that the developers have to identify which part of the application cannot be offloaded; 2) effectiveness: the reduced execution time must be bigger than the network delay caused by computation offloading so that the developers have to calculate which part is worthy to offload; 3) adaptability: smartphone apps always face with changes of user requirements and runtime environments so that the developers have to implement the adaptation on offloading. More importantly, considering the large number of today's smartphone applications, the solution applicable for legacy applications will be much more valuable. In this paper, we present a tool automatically refactoring Android applications to be the ones with computation offloading capability. For a given Android app, the tool at first analyzes the bytecodes for discovering the parts worthy to offload, then rewrites the bytecodes to implement the special program structure supporting flexible offloading, and finally generates two packages to be deployed into the Android phone and the server respectively. This tool is experimented on a set of real-world Android applications, reducing the execution time by 46%-97% and the battery energy consumption by 27%-83%.
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Submitted on : Wednesday, November 14, 2012 - 5:27:09 AM
Last modification on : Thursday, May 28, 2020 - 11:58:05 PM
Long-term archiving on: : Saturday, December 17, 2016 - 10:16:33 AM


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  • HAL Id : hal-00751656, version 1



Ying Zhang, Gang Huang, Xuanzhe Liu, Wei Zhang, Hong Mei, et al.. Refactoring Android Java Code for On-Demand Computation Offloading. OOPSLA 2012, Oct 2012, Tucson Arizona, United States. ⟨hal-00751656⟩



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