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2021_IEEEIoT_Task Offloading Optimization.pdf (5.77 MB)

Distributed task offloading optimization with queueing dynamics in multi-agent mobile-edge computing networks

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journal contribution
posted on 2023-06-09, 23:12 authored by Jianshan Zhou, Daxin Tian, Zhengguo ShengZhengguo Sheng, Xuting Duan, Xuemin Shen
Task offloading decision-making plays a key role in enabling mobile-edge computing (MEC) technologies in Internet-of-Things (IoT). However, it meets the significant challenges arising from the stochastic dynamics of task queueing in the application layer and coupled wireless interference in the physical layer in a distributed multi-agent network without any centralized communication and computing coordination. In this paper, we investigate the distributed task offloading optimization problem with consideration of the upper-layer queueing dynamics and the lower-layer coupled wireless interference. We first propose a new optimization model that aims at maximizing the expected offloading rate of multiple agents by optimizing their offloading thresholds. Then, we transform the problem into a game-theoretic formulation, which further leads to the design of a distributed best-response (DBR) iterative optimization framework. The existence of Nash equilibrium strategies in the game-theoretic model has been analyzed. For the individual optimization of each agent’s threshold policy, we further propose a programming scheme by transforming a constrained threshold optimization into an unconstrained Lagrangian optimization (ULO). The individual ULO is integrated into the DBR framework to enable agents to cooperate and converge to a global optimum in a distributed manner. Finally, simulation results are provided to validate the proposed method and demonstrate its significant advantage over other existing distributed methods. The numerical results also show that the proposed method can achieve comparable performance to a centralized optimization method.

History

Publication status

  • Published

File Version

  • Accepted version

Journal

IEEE Internet of Things Journal

ISSN

2327-4662

Publisher

Institute of Electrical and Electronics Engineers

Department affiliated with

  • Engineering and Design Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2021-03-02

First Open Access (FOA) Date

2021-03-11

First Compliant Deposit (FCD) Date

2021-03-01

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