Optimization Of Energy Consumption In Mobile Device Using Mobile Cloud Computing

Mobile Cloud Computing is a new concept of Cloud Computing with a mobility feature, and it is a way to overcome the limitation of mobile devices, such as the low computational power and insufficient memory capabilities. With the booming development of mobile communication, various functional applications can be easily downloaded from the application venders. At the same time, mobile device can offload some applications over wireless internet to the cloud center for execution, which greatly reduces the requirements for storage and computing in mobile devices.However, in recent years, the amount of energy that can be stored in a battery is limited and is growing only at the rate of 5% every year. Compared with the booming growth in mobile applications, limited battery capacity has become the largest short board in the performance of intelligent mobile equipment. Therefore, how to reduce energy consumption of mobile devices in a wireless cloud environment has gradually become the focus of the research.Currently, the research direction of the green IT based on Mobile Cloud Computing mainly focused on two points, how to upload the applications of mobile devices to cloud computing center, and when to upload applications. For the former research, local optimization algorithms are usually adopted to optimize the energy consumption of uploading, for the latter, energy prediction is usually used to determine when to upload can save more energy. However, these existing schemes are mainly based on the experience formula between energy and uploading rate, and the energy prediction has not considered the effect of channel gain quantization error. What’s more, they lack of specific analysis of the actual scene.According to the problems above, this paper makes the following contributions. First, the asymptotic time complexity is applied to distinguish the computational complexities of the applications in order to optimize the energy consumed by the CPUs in mobile devices. The multi-scale scheme is adopted to quantize the channel gain, which is proved to be helpful for reducing the mobile devices energy consumption when offloading the applications to the cloud center. The analytical results can be used as a guideline for the mobile devices to choose whether to execute the application locally or to offload it to the cloud center. Second, in order to save more energy while uploading, a two-step approach is adopted in OFDMA system, allocating the transmission bit rate both among different time slots and sub-channels.