Framework for context-aware computation offloading in mobile cloud computing

Xing Chen, Shihong Chen, Xuee Zeng, Xianghan Zheng, Ying Zhang, Chunming Rong
2017 Journal of Cloud Computing: Advances, Systems and Applications  
Computation offloading is a promising way to improve the performance as well as reducing the battery power consumption of a mobile application by executing some parts of the application on a remote server. Recent researches on mobile cloud computing mainly focus on the code partitioning and offloading techniques, assuming that mobile codes are offloaded to a prepared server. However, the context of a mobile device, such as locations, network conditions and available cloud resources, changes
more » ... ources, changes continuously as it moves throughout the day. And applications are also different in computation complexity and coupling degree. So it needs to dynamically select the appropriate cloud resources and offload mobile codes to them on demand, in order to offload in a more effective way. Supporting such capability is not easy for application developers due to (1) adaptability: mobile applications often face changes of runtime environments so that the adaptation on offloading is needed. (2) effectiveness: when the context of the mobile device changes, it needs to decide which cloud resource is used for offloading, and the reduced execution time must be greater than the network delay caused by offloading. This paper proposes a framework, which supports mobile applications with the context-aware computation offloading capability. First, a design pattern is proposed to enable an application to be computation offloaded on-demand. Second, an estimation model is presented to automatically select the cloud resource for offloading. Third, a framework at both client and server sides is implemented to support the design pattern and the estimation model. A thorough evaluation on two real-world applications is proposed, and the results show that our approach can help reduce execution time by 6-96% and power consumption by 60-96% for computation-intensive applications.
doi:10.1186/s13677-016-0071-y fatcat:trhla3kwyfgadiukpcfwd3qkri