Mobile devices are constrained by the limited capacities of their small batteries. However, profiling the energy consumed in the task execution is crucial to help the developers to build energy efficient applications. Therefore, the major challenge in the profiling approach is to accurately estimating the energy consumed for an application by the hardware components, such as CPU, memory, storage unit, and network interfaces. In this work, we develop and validate hardware and software profiling models and procedures.

Task offloading from smartphones to the cloud is a promising strategy to enhance the computing capability of smartphones and prolong their battery life. However, task offloading introduces a communication cost for those devices. Therefore, the consideration of the communication cost is crucial for the effectiveness of task offloading. To make task offloading beneficial, one of the challenges is to estimate the energy consumed in communication activities of task offloading.

In this paper, we propose a practical decision engine to offload tasks from mobile devices to the cloud to realize the concept of Energy-as-a-Service. We implemented the decision engine on an Android smartphone and an Amazon ES2 and S3 clouds to conduct experiments to determine the values of system parameters used by the decision engine. The practicality of the decision engine is demonstrated by means of real-world computing scenarios.

In spite of the dramatic growth in the number of smartphones in recent years, the challenge of limited energy capacity of these devices has not been solved satisfactorily. However, in the era of cloud computing, the limitation on energy capacity can be eased off in an efficient way by offloading heavy tasks to the cloud. It is important for smartphone and cloud computing developers to have insights into the energy cost of smartphone applications before implementing the offloading techniques.

In spite of the dramatic growth in the number of smartphones in the recent years, the energy capacity challenge for these devices has not been solved satisfactorily. Moreover, the global demand for green Information and Communication Technology (ICT) motivates the researchers to consider cloud computing as a new computing paradigm that is promising for green solution. In this paper, we propose new green solutions that save smartphones energy and at the same time achieve the green ICT goal. Our green solution is achieved by what we call Mobile Cloud Computing (MCC).