Distributed Multi-Objective Dynamic Offloading Scheduling for Air-Ground Cooperative MEC

• URL: http://arxiv.org/abs/2403.10927v1
• Date: Sat, 16 Mar 2024 13:50:31 GMT
• Title: Distributed Multi-Objective Dynamic Offloading Scheduling for Air-Ground Cooperative MEC
• Authors: Yang Huang, Miaomiao Dong, Yijie Mao, Wenqiang Liu, Zhen Gao,
• Abstract summary: This paper proposes a distributed trajectory planning and offloading scheduling scheme, integrated with MORL and the kernel method. Numerical results reveal that the n-step return can benefit the proposed kernel-based approach, achieving significant improvement in the long-term average backlog performance.
• Score: 13.71241401034042
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Utilizing unmanned aerial vehicles (UAVs) with edge server to assist terrestrial mobile edge computing (MEC) has attracted tremendous attention. Nevertheless, state-of-the-art schemes based on deterministic optimizations or single-objective reinforcement learning (RL) cannot reduce the backlog of task bits and simultaneously improve energy efficiency in highly dynamic network environments, where the design problem amounts to a sequential decision-making problem. In order to address the aforementioned problems, as well as the curses of dimensionality introduced by the growing number of terrestrial terrestrial users, this paper proposes a distributed multi-objective (MO) dynamic trajectory planning and offloading scheduling scheme, integrated with MORL and the kernel method. The design of n-step return is also applied to average fluctuations in the backlog. Numerical results reveal that the n-step return can benefit the proposed kernel-based approach, achieving significant improvement in the long-term average backlog performance, compared to the conventional 1-step return design. Due to such design and the kernel-based neural network, to which decision-making features can be continuously added, the kernel-based approach can outperform the approach based on fully-connected deep neural network, yielding improvement in energy consumption and the backlog performance, as well as a significant reduction in decision-making and online learning time.

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