Service Placement and Trajectory Design for Heterogeneous Tasks in Multi-UAV Cooperative Computing Networks

TL;DR

This paper proposes a reinforcement learning-based approach for optimizing UAV trajectories and resource allocation to minimize energy consumption in heterogeneous task scenarios within multi-UAV MEC networks.

Contribution

It introduces a novel joint optimization framework for service placement, task scheduling, and trajectory design using a soft actor-critic algorithm in dynamic UAV networks.

Findings

Significant reduction in system energy consumption achieved.

Effective handling of task heterogeneity and dynamic wireless channels.

Outperforms baseline solutions in numerical simulations.

Abstract

In this paper, we consider deploying multiple Unmanned Aerial Vehicles (UAVs) to enhance the computation service of Mobile Edge Computing (MEC) through collaborative computation among UAVs. In particular, the tasks of different types and service requirements in MEC network are offloaded from one UAV to another. To pursue the goal of low-carbon edge computing, we study the problem of minimizing system energy consumption by jointly optimizing computation resource allocation, task scheduling, service placement, and UAV trajectories. Considering the inherent unpredictability associated with task generation and the dynamic nature of wireless fading channels, addressing this problem presents a significant challenge. To overcome this issue, we reformulate the complicated non-convex problem as a Markov decision process and propose a soft actor-critic-based trajectory optimization and resource allocation algorithm to implement a flexible learning strategy. Numerical results illustrate that within a multi-UAV-enabled MEC network, the proposed algorithm effectively reduces the system energy consumption in heterogeneous tasks and services scenarios compared to other baseline solutions.