Task offloading to mobile edge computing (MEC) has emerged as a key technology to alleviate the computation workloads of mobile devices and decrease service latency for the computation-intensive applications. Device battery consumption is one of the limiting factors needs to be considered during task offloading. In this paper, multi-task offloading strategies have been investigated to improve device energy efficiency. Correlations among tasks in time domain as well as task domain are proposed to be employed to reduce the number of tasks to be transmitted to MEC. Furthermore, a binary decision tree based algorithm is investigated to jointly optimize the mobile device clock frequency, transmission power, structure and number of tasks to be transmitted. MATLAB based simulation is employed to demonstrate the performance of our proposed algorithm. It is observed that the proposed dynamic multi-task offloading strategies can reduce the total energy consumption at device along various transmit power versus noise power point compared with the conventional one.
翻译:将任务从卸载到移动边缘计算(MEC)已成为减轻移动设备工作量和减少计算密集型应用服务延迟度的关键技术,在卸载任务时需要考虑限制因素之一,设备电池消耗是限制因素之一,在本文件中,对多任务卸载战略进行了调查,以提高设备能效,提议采用时间领域和任务领域任务之间的相互交错,以减少向MEC转交的任务数量。此外,还调查基于二进制决策树的算法,以联合优化移动设备时钟频率、传输功率、结构以及所要传输的任务数量。基于MATLAB的模拟用于展示我们拟议的算法的性能。据观察,拟议的动态多任务卸载战略可以减少各种传输功率和噪音功率点在各种传输装置上的能源消耗总量。