A Coded Distributed Computing Framework for Task Offloading from Multi-UAV to Edge Servers

Yunkai Guo, Shushi Gu, Qinyu Zhang, Ning Zhang, Wei Xiang

Abstract: Unmanned aerial vehicles (UAVs) have been widely used in wireless edge networks for task offloading, with the advantages of their agile management and high-flexibility deployment. However, due to limited computation capability and restricted battery life, processing computation-intensive tasks on board may cause the excessive cost of latency and energy. In this paper, we propose a novel framework with coded distributed computing (CDC) for the task offloading from multi-UAV to ground edge servers, which can save transmitting and flying energy consumption in the air, and reduce computation latency in the terrestrial distributed server networks with stragglers. Specifically, we formulate a latency-energy cost minimization problem, to obtain the optimal the UAVs' trajectory schedule and the appropriate CDC's parameters. Moreover, we divide this problem into two sub-optimization problems, which are solved by a cost optimal trajectory schedule (COTS) algorithm and a cost optimal code parameter design (COCPD) algorithm, respectively. Finally, numerical results indicate the feasibility and the effectiveness of our proposed framework, which also validate that CDC can significantly reduce the cost in the UAV edge computing network.