Delay-Aware Cooperative Task Offloading for Multi-UAV Enabled Edge-Cloud Computing

Unmanned aerial vehicle (UAV) has received tremendous attention in the area of edge computing due to its flexible deployment and wide coverage accessibility. In weak infrastructure scenarios, multiple UAVs can form on-site edge computing clusters to handle the real-time tasks. Further, a multi-UAV enabled edge-cloud computing system is coined by cooperating the UAVs with remote cloud, which provides superior computing capability. However, the uneven distribution of tasks makes it difficult to meet the real-time requirements when load balancing is unavailable. To address above issue, a delay minimization problem for multi-UAV enabled edge-cloud cooperative offloading is investigated in this paper. The problem is formulated as a non-convex problem based on models that reflect characteristics of the system, such as ubiquitous network congestion, air-to-ground wireless channel and cooperative parallel computing. An efficient cooperative offloading algorithm is proposed to address the problem. Specifically, convex approximation is applied to make the original problem tractable, and Lyapunov optimization is utilized to make online task offloading decisions. Finally, the correctness of the models are verified through a practical UAV-edge computing platform. Simulations based on measurement results and real-world datasets indicate that, the proposed algorithm fully utilizes the available energy to significantly reduce the tasks’ completion delay.

1 Introduction

Unmanned aerial vehicle (UAV) has been widely used in special scenarios by telecommunication companies such as Qualcomm, Ericsson and China Mobile [1]. When performing monitoring and rescuing missions in the post-natural disaster scenarios (e.g., fixed infrastructures have been disrupted by earthquakes), UAV is able to fly to the site to collect essential data for subsequent risk assessment. The missions demand for the real-time analysis of the complicated data. Therefore, the concept of UAV-EC (i.e., UAV enabled edge computing) has been proposed [2]. The main feature of edge computing (EC) is to push computing to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited scenarios. Compared to cloud computing, UAV-enabled edge computing can effectively reduce the transmission delay caused by extremely long distance [3]. As Huawei’s whitepaper shows, UAV technology combined with edge computing will enable emerging areas like AI and remote sensing to new levels of automation and new types of analytic solutions [4].