Service Caching Based Aerial Cooperative Computing and Resource Allocation in Multi-UAV Enabled MEC Systems

Service caching refers to caching the necessary programs or/and the related databases for performing computational tasks at edge servers, which has been considered to save both computation and communication resources in mobile edge computing (MEC) systems. In this paper, we investigate computation service caching in a multi-unmanned aerial vehicle (UAV) enabled MEC system, where each UAV equipped with an edge server acts as an aerial computing platform to serve the ground devices. Furthermore, the UAVs can serve the devices cooperatively through the provided computing and caching resources. Aiming at minimizing the maximum task completion latency among all devices, we formulate a joint service caching, task offloading, communication and computation resource allocation, as well as UAV placement optimization problem, while guaranteeing the energy budget of all devices and UAVs. The problem is a mixed integer non-linear programming problem, and we decompose it into four sub-problems, and then propose an iterative algorithm based on block coordinate descent (BCD) and successive convex approximation (SCA) techniques to obtain near-optimal solution. Numerical results show that our proposed algorithm can achieve lower task completion latency than other baselines while guaranteeing better fairness among all devices.

I. Introduction

The rapid developments of mobile networks and Internet of Things (IoT) have accelerated the spread of various intelligent applications (e.g., augmented reality (AR), virtual reality (VR), face recognition, interactive gaming, etc.), which require extremely high computational load to achieve low-latency services. IoT devices, however, have limited computing ability as well as battery capacity and thereby cannot meet the demanding requirements of these computing-intensive and low-latency applications. Mobile edge computing (MEC) has been considered as a promising technology to handle this challenge by deploying high-performance servers at the edge of wireless networks [2]. With MEC, IoT devices can offload their computational tasks to the edge servers, thereby saving energy of devices while meeting the delay requirement [3]. In recent years, there have been considerable studies in MEC networks that investigate computation offloading, computing and communication resource allocation to improve system performance [4]–[7]. However, these works only study the scenario that the MEC servers are deployed in fixed locations (e.g., base station (BS) or access point (AP)) and cannot be applied to the situation where the devices are out of the infrastructure coverage or the network facilities are destroyed.