An Adaptive Computation Offloading Decision for Energy-Efficient Execution of Mobile Applications in Clouds

In recent years, computation offloading, through which applications on a mobile device can offload their computations onto more resource-rich clouds, has emerged as a promising technique to reduce battery consumption as well as augment the devices’ limited computation and memory capabilities. In order for computation offloading to be energyefficient, an accurate estimate of battery consumption is required to decide between local processing and computation offloading. In this paper, we propose a novel technique for estimating battery consumption without requiring detailed information about the mobile application’s internal structure or its execution behavior. In our approach, the relationship is derived between variables that affect battery consumption (i.e., the input to the application, the transmitted data, and resource status) and the actual consumed energy from the application’s past run history. We evaluated the performance of the proposed technique using two different types of mobile applications over different wireless network environments such as 3G, WiFi, and LTE. The experimental results show that our technique can provide tolerable estimation accuracy and thus make correct decisions between local processing and computation offloading. key words: battery consumption, dynamic estimation, linear regression, mobile clouds