Online Computation Offloading and Traffic Routing for UAV Swarms in Edge-Cloud Computing
Baichuan Liu, Weikun Zhang, Wuhui Chen, Huawei Huang, Song Guo
Abstract
Unmanned Aerial Vehicle (UAV) swarms are widely applied for service provisioning in many domains such as topographic mapping and traffic monitoring. These UAV applications are complicated as they demand not only huge computational resources but also strict latency requirements. However, due to the limited onboard resources of UAVs as well as various dynamics of applications and environments, it's challenging to obtain a feasible computation offloading policy in online environments. To cope with these problems, we propose an online algorithm for UAV swarms to jointly optimize the computation offloading and multi-hop routing scheduling in the Edge-Cloud environment. First, we propose a UAV-Edge-Cloud computing model to meet the requirements of latency and computing capability. We then formulate a joint optimization of workflow assignment and multi-hop routing scheduling in order to minimize the computation cost and latency. To tackle this NP-hard problem, an algorithm based on the Markov approximation technique is designed to obtain near-optimal solutions. Furthermore, to achieve high long-term performance in online environments, we exploit Lyapunov optimization to control the migration cost caused by the dynamics of application and environment. Finally, extensive simulation results demonstrate that our approach achieves a long-term high-efficiency and stable performance in an online environment for UAV swarms with respect to computation offloading and traffic routing.