Mobile offloading is an effective way that migrates computation-intensive parts of applications from resource-constrained mobile devices onto remote resource-rich servers  . Application partitioning plays a critical role in high-performance offloading systems, which involves splitting the execution of applications between the mobile side and cloud side so that the total execution cost is minimized. Through partitioning, the mobile device can have the most benefit from offloading the application to a remote cloud. In this paper, we study how to effectively and dynamically partition a given application into local and remote parts while keeping the total cost as small as possible. For general tasks (i.e., arbitrary topological consumption graphs), we propose a new min-cost offloading partitioning (MCOP) algorithm that aims at finding the optimal application partitioning (determining which portions of the application to run on mobile devices and which portions on cloud servers) under different partitioning cost models and mobile environments. The simulation results show that the proposed algorithm provides a stably low time complexity method and can significantly reduce execution time and energy consumption by optimally distributing tasks between mobile devices and cloud servers, and in the meantime, it can well adapt to environment changes.
Mobile cloud offloading that migrates computation-intensive parts of applications from resource-constrained mobile devices onto remote resource-rich servers , is an effective way to shorten response time and extend battery life of mobile devices . Application partitioning plays a critical role in high-performance offloading systems, which involves splitting the execution of applications between the mobile side and cloud side so that the total execution cost is minimized. Through partitioning, the mobile device can have the most benefit from offloading the application to a remote cloud. In this paper, we study how to effectively and dynamically partition a given application into local and remote parts while keeping the total cost as small as possible. For general tasks (i.e., arbitrary topological consumption graphs), we propose a novel min-cost offloading partitioning (MCOP) algorithm that aims at finding the optimal partitioning plan (determining which portions of the application to run on mobile devices and which portions on cloud servers) under different cost models and mobile environments. The simulation results show that the proposed algorithm provides a stably low time complexity method and can significantly reduce execution time and energy consumption by optimally distributing tasks between mobile devices and cloud servers, and in the meantime, it can well adapt to environment changes.
R
offloading is an effective way
cloud offloading
A
, is an effective way to shorten response time and extend battery life of mobile devices
R
new
novel
R
application partitioning
partitioning plan
D
partitioning