TY - JOUR

T1 - Run Time Application Repartitioning in Dynamic Mobile Cloud Environments

AU - Yang, L.

AU - Cao, J.

AU - Tang, S.

AU - Han, D.

AU - Suri, Neeraj

N1 - Cited By :20 Export Date: 7 October 2019

PY - 2016/7/1

Y1 - 2016/7/1

N2 - 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. © 2013 IEEE.

AB - 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. © 2013 IEEE.

KW - application partitioning

KW - Mobile cloud computing

KW - repartitioning

KW - trajectory matching

KW - Costs

KW - Mobile computing

KW - Mobile devices

KW - Application execution

KW - Application partitioning

KW - Mobile applications

KW - Mobile cloud applications

KW - Network connection

KW - Performance degradation

KW - Trajectory matching

U2 - 10.1109/TCC.2014.2358239

DO - 10.1109/TCC.2014.2358239

M3 - Journal article

VL - 4

SP - 336

EP - 348

JO - IEEE Transactions on Cloud Computing

JF - IEEE Transactions on Cloud Computing

SN - 2168-7161

IS - 3

ER -