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

2025-10-22

AI Summary

This paper addresses the problem of minimizing system cost—defined as a weighted sum of task latency and energy consumption—under task dependency constraints in multi-UAV-enabled mobile edge computing. To this end, we propose a joint optimization framework for subtask offloading, computational resource allocation, and UAV trajectory design. Our method introduces a novel two-timescale decomposition mechanism and models task dependencies using a directed acyclic graph (DAG). To tackle the resulting non-convex mixed-integer nonlinear programming (MINLP) problem, we develop a hybrid PDD-SCA algorithm that integrates penalty dual decomposition (PDD) with successive convex approximation (SCA). Experimental results demonstrate that the proposed approach significantly reduces overall system cost, achieves a superior latency–energy trade-off, and enhances computational load balancing across multiple UAVs.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Optimizing task offloading in multi-UAV edge computing systems
• Minimizing task completion delay and system energy consumption
• Managing dependencies among serial and parallel sub-tasks

Innovation

• UAV-assisted collaborative MEC framework with task decomposition
• Two-timescale DAG modeling for sub-task dependency decoupling
• PDD-SCA algorithm for joint optimization of offloading and trajectories