Energy-Aware Fault Tolerant Task offloading of Mobile Cloud Computing

With all the hardware advances that have been achieved lately relating to hand-held mobile devices, still resource-intensive applications consider an important issue. The heavy computational tasks of these applications cannot be processed in the mobile device itself because of their limited processing and storage capabilities. Recently, many attempts have been achieved to handle this issue. Most of these attempts are depending on utilizing remote servers of the cloud environment. This process which takes advantageous of cloud services allows mobile users offloading their computationally complicated tasks to be processed in remote servers of cloud environment, giving the birth to what is called mobile cloud computing model. Despite the benefits that outcome from task offloading process, challenges of energy efficiency (e.g. energy consumption for task processing), reliability (e.g. node failure), and time management (e.g. task deadline and execution time) still need to be significantly addressed. In this paper, we propose a novel scheduling technique based on DNA combinations and genetic algorithm processing under the precedence level. This technique is suggested to decrease the ratio of energy consumption, minimize the processing time of the task execution without exceeding the task deadline, and provide reliability by retrieving the processed data successfully by the mobile device user and avoid task failure in mobile cloud computing model.