Framework for Context-aware Computation Offloading in Mobile Cloud Computing

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 and network conditions, changes continuously as it moves throughout the day; and there are multiple options of cloud resources, including remote cloud computing services and nearby cloudlets. In order to offload computation to the cloud resource with powerful processors as well as fast network connection, it needs to dynamically select the appropriate cloud resource and then offload mobile codes to it at runtime, according to the context of the mobile device and possible cloud resources. In this paper, we present a framework for context-aware computation offloading. 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 computation offloading. Runtime data about computation tasks, contexts of the mobile device and possible cloud resources is collected and modeled at client side, in order to make an optimal offloading decision. 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.