Mobile computation offloading has recently attracted much interest and first offloading solutions have been developed. However, the relevant technical challenge of how to automatically determine offloadable sections of Android applications has not been adequately investigated so far. This paper proposes an innovative task selection algorithm that can parse an Android application autonomously and classify all the methods based on their offloadability by adopting a finegrained and multi-steps analyzer. The reported experimental results show the effectiveness of our solution when applied to the top 25 most downloaded Android apps on the Google Play store, by showing its accuracy in identifying offloadable methods and demonstrating the potential benefits of automated mobile computation offloading.

Automated selection of offloadable tasks for mobile computation offloading in edge computing

Zanni, Alessandro;Bellavista, Paolo;
2017

Abstract

Mobile computation offloading has recently attracted much interest and first offloading solutions have been developed. However, the relevant technical challenge of how to automatically determine offloadable sections of Android applications has not been adequately investigated so far. This paper proposes an innovative task selection algorithm that can parse an Android application autonomously and classify all the methods based on their offloadability by adopting a finegrained and multi-steps analyzer. The reported experimental results show the effectiveness of our solution when applied to the top 25 most downloaded Android apps on the Google Play store, by showing its accuracy in identifying offloadable methods and demonstrating the potential benefits of automated mobile computation offloading.
13th International Conference on Network and Service Management (CNSM), 2017
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Zanni, Alessandro; Se-Young, Yu; Bellavista, Paolo; Langar, Rami; Secci, Stefano
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/619489
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