A Task-Centric Mobile Cloud-Based System to Enable Energy-Aware Efficient Offloading

To support increasingly sophisticated sensors and resource-hungry applications with the current-used Lithium-based batteries and to augment mobile computing power further, the concept of the Cloudlet-based offloading is proposed which enables to migrate part of application computing tasks from battery-limited low-capacity mobile elements to local Cloudlets. However, due to the limited processing capability and the lack of fine-grain resource management schemes on the Cloudlet, the Cloudlet resources can be quickly overloaded especially in the large-scale multi-user offloading scenarios. As a result, a considerable number of offloading requests are forwarded to the remote Cloud, which may significantly increase the communication overhead for the energy-sensitive mobile offloading tasks. In this paper, we develop and formulate a novel task-centric energy-aware Cloudlet-based Mobile Cloud model to address this issue. We concern the offloading performance, scalability, security, and availability problems, aiming at increasing the Cloudlet processing throughput, reducing the energy cost on the remote Cloud, and improving offloading execution efficiency and energy-efficiency on the mobile devices. A Cloudlet task-based offloading mechanism is proposed to achieve fine-grain energy-aware offloading resource preparation and scheduling on the Cloudlet. A Cloud task-centric scheduling algorithm is presented for the green collaborative offloading processing between Cloudlet and remote Cloud. The experiment results demonstrate that the energy-aware offloading model can efficiently enhance offloading performance for mobile devices, and the offloading scheduling schemes for the Cloudlet and remote Cloud outperform the traditional protocol class.