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Title: Run time application repartitioning in dynamic mobile cloud environments
Authors: Yang, L 
Cao, J 
Tang, S
Han, D
Suri, N
Keywords: Application partitioning
Mobile cloud computing
Trajectory matching
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on cloud computing, 2016, v. 4, no. 3, 6898822, p. 336-348 How to cite?
Journal: IEEE transactions on cloud computing 
Abstract: As mobile computing increasingly interacts with the cloud, a number of approaches, e.g., MAUI and CloneCloud, have been proposed, aiming to offload parts of the mobile application execution to the cloud. To achieve a good performance by using these approaches, they particularly focus on the application partitioning problem, i.e., to decide which parts of an application should be offloaded to the cloud and which parts should be executed on mobile devices such that the execution cost is minimized. Most works on this problem assume that the offloading cost of each part of the application remains the same as the application is running. Unfortunately, this assumption does not hold in dynamic mobile cloud environments, where the device and network connection status may fluctuate, and thus affects the offloading cost. With the varying offloading cost, the one time partitioning of the application may yield significant performance degradations. In this paper, we study application repartitioning problem which considers updating the partition periodically during the course of application execution. We first propose a framework for run time application repartitioning in dynamic mobile cloud environments. Based on this framework, we take the dynamic network connection to clouds as a case study, and design an online solution, Foreseer, to solve the mobile cloud application repartitioning problem. We evaluate our solution based on real world data traces that are collected in a campus WiFi hotspot testbed. The result shows that our method can achieve significantly shorter completion time over previous approaches.
ISSN: 2168-7161
DOI: 10.1109/TCC.2014.2358239
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