Dependency-Aware Task Offloading in Multi-UAV Assisted Collaborative Mobile Edge Computing

TL;DR

This paper introduces a dependency-aware multi-UAV MEC framework that optimizes task offloading, resource allocation, and UAV trajectories to reduce system cost and improve workload balancing, using advanced optimization techniques.

Contribution

It presents a novel framework modeling task dependencies with DAGs and develops a PDD-SCA algorithm for joint optimization in multi-UAV MEC systems.

Findings

Significant reduction in system cost compared to benchmarks.

Improved trade-off between task latency and energy consumption.

Efficient workload balancing across multiple UAVs.

Abstract

This paper proposes a novel multi-unmanned aerial vehicle (UAV) assisted collaborative mobile edge computing (MEC) framework, where the computing tasks of terminal devices (TDs) can be decomposed into serial or parallel sub-tasks and offloaded to collaborative UAVs. We first model the dependencies among all sub-tasks as a directed acyclic graph (DAG) and design a two-timescale frame structure to decouple the sub-task interdependencies for sub-task scheduling. Then, a joint sub-task offloading, computational resource allocation, and UAV trajectories optimization problem is formulated, which aims to minimize the system cost, i.e., the weighted sum of the task completion delay and the system energy consumption. To solve this non-convex mixed-integer nonlinear programming (MINLP) problem, a penalty dual decomposition and successive convex approximation (PDD-SCA) algorithm is developed. Particularly, the original MINLP problem is equivalently transferred into a continuous form relying on PDD theory. By decoupling the resulting problem into three nested subproblems, the SCA method is further combined to recast the non-convex components and obtain desirable solutions. Numerical results demonstrate that: 1) Compared to the benchmark algorithms, the proposed scheme can significantly reduce the system cost, and thus realize an improved trade-off between task latency and energy consumption; 2) The proposed algorithm can achieve an efficient workload balancing for distributed computation across multiple UAVs.