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DRL Based Computation Efficiency Maximization in MEC-Enabled Heterogeneous Networks
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DOI：10.1109/TVT.2024.3403731.png)
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Abstract
En 中文
Mobile edge computing (MEC)-enabled heterogeneous networks which compose of multiple layers with different MEC servers can support massive computation-intensive and delay-sensitive applications by pushing cloud functions to the edge of the networks. The heterogeneous nature of networks and the different transmit powers, computing capabilities, coverage areas and various sizes tasks of MEC servers make the resources (such as communication and computation) allocation become more challenging. More importantly, facing the ever-increasing greenhouse gas emission concerns and the rapid growth of the operational cost, how to effectively utilize the resources to improve computation efficiency is of vital importance. In this paper, with the aim to maximize the computation efficiency of the MEC-enabled heterogeneous networks through proper resource allocation, a joint task offloading and resource management problem is formulated. The formulated problem turns out to be a mixed integer nonlinear programming (MINLP) problem. To solve it, an Advantage Actor-Critic (A2C)-based on joint task offloading and resource allocation algorithm (A2C-JTRA) is proposed to determine the offloading strategy, transmit power control and the central processing unit (CPU) computing frequencies allocation of MEC servers and terminal devices (TDs). Simulation results demonstrate that the superiority of the proposed algorithm in improving the computation efficiency compared to the baseline algorithms.
Keywords:
Task analysis
Servers
Resource management
Heterogeneous networks
Energy consumption
Central Processing Unit
Computational modeling
Mobile edge computing (MEC)
heterogeneous networks
computation efficiency
deep reinforcement learning (DRL)
Journal
IF:
7.1
Papers:
1.8W
Citations:
6.6W