4.8 Article

Cooperative Multiagent Deep Reinforcement Learning for Computation Offloading: A Mobile Network Operator Perspective

期刊

IEEE INTERNET OF THINGS JOURNAL
卷 9, 期 23, 页码 24161-24173

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2022.3189445

关键词

Computational offloading; deep reinforcement learning (DRL); delay bounds; mobile-edge computing (MEC); task revenue

资金

  1. National Key Research and Development Program of China [2019YFB1802800]
  2. National Natural Science Foundation of China [62032013, 61872073]
  3. Major International(Regional) Joint Research Project of NSFC [71620107003]
  4. Liaoning Revitalization Talents Program [XLYC1902010]

向作者/读者索取更多资源

Computation offloading decisions are crucial for implementing mobile-edge computing in IoT services. This article investigates the optimization problem of multiuser delay-sensitive tasks from the perspective of mobile network operators. It proposes a new optimization model and a cooperative multiagent deep reinforcement learning algorithm to achieve higher profit and better computational performance.
Computation offloading decisions play a crucial role in implementing mobile-edge computing (MEC) technology in the Internet of Things (IoT) services. Mobile network operators (MNOs) can employ computation offloading techniques to reduce task completion delay and improve the Quality of Service (QoS) for users by optimizing the system's processing delay and energy consumption. However, different IoT applications (e.g., entertainment and autonomous driving) generate different delay tolerances and benefits for computational tasks from the MNO perspective. Therefore, simply minimizing the delay of all tasks does not satisfy the QoS of each user. The system architecture design should consider the significance of users and the heterogeneity of tasks. Unfortunately, rare work has been done to discuss this practical issue. In this article, from the perspective of MNO, we investigate the computation offloading optimization problem of multiuser delay-sensitive tasks. First, we propose a new optimization model, which designs different optimization objectives for the cost and revenue of tasks. Then, we transform the problem into a Markov decision processes problem, which leads to designing a multiagent iterative optimization framework. For the strategic optimization of each agent, we further propose a cooperative multiagent deep reinforcement learning (CMDRL) algorithm to optimize two different objectives at the same time. Two agents are integrated into the CMDRL framework to enable agents to collaborate and converge to the global optimum in a distributed manner. At the same time, the priority experience replay method is introduced to improve the utilization rate of effective samples and the learning efficiency of the algorithm. The experimental results show that our proposed method can effectively achieve a significantly higher profit than the alternative state-of-the-art method and exhibit a more favorable computational performance than benchmark deep reinforcement learning methods.

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