Collaboration in the Sky: A Distributed Framework for Task Offloading and Resource Allocation in Multi-Access Edge Computing

• Published in: IEEE internet of things journal 2022-12, Vol.9 (23), p.24221-24235
• Main Authors: Tun, Yan Kyaw, Dang, Tri Nguyen, Kim, Kitae, Alsenwi, Madyan, Saad, Walid, Hong, Choong Seon
• Format: Article
• Language: English
• Subjects: Alternating direction method of multipliers (ADMMs); Autonomous aerial vehicles; Base stations; Collaboration; collaborative multiunmanned aerial vehicle (UAV)-assisted MEC system; Computation offloading; Decomposition; Edge computing; Lagrangian relaxation; Mixed integer; Mobile computing; Multi-access edge computing; multi-access edge computing (MEC); Nonlinear programming; Resource allocation; Resource management; Servers; Task analysis; tasks offloading; Unmanned aerial vehicles
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2022.3189000

Recently, unmanned aerial vehicles (UAVs)-assisted multi-access edge computing (MEC) systems emerged as a promising solution for providing computation services to mobile users outside of terrestrial infrastructure coverage. As each UAV operates independently, however, it is challenging to meet the computation demands of the mobile users due to the limited computing capacity at the UAV's MEC server as well as the UAV's energy constraint. Therefore, collaboration among UAVs is needed. In this article, a collaborative multi-UAV-assisted MEC system integrated with an MEC-enabled terrestrial base station (BS) is proposed. Then, the problem of minimizing the total latency experienced by the mobile users in the proposed system is studied by optimizing the offloading decision as well as the allocation of communication and computing resources while satisfying the energy constraints of both mobile users and UAVs. The proposed problem is shown to be a nonconvex, mixed-integer nonlinear programming (MINLP) problem that is intractable. Therefore, the formulated problem is decomposed into three subproblems: 1) users tasks offloading decision problem; 2) communication resource allocation problem; and 3) UAV-assisted MEC decision problem. Then, the Lagrangian relaxation and alternating direction method of multipliers (ADMMs) methods are applied to solve the decomposed problems, alternatively. Simulation results show that the proposed approach reduces the average latency by up to 40.7% and 4.3% compared to the greedy and exhaustive search methods.