An Intelligent Offloading System Based on Multiagent Reinforcement Learning

Intelligent vehicles have provided a variety of services; there is still a great challenge to execute some computing-intensive applications. Edge computing can provide plenty of computing resources for intelligent vehicles, because it offloads complex services from the base station (BS) to the edge...

Full description

Saved in:
Bibliographic Details
Published in:Security and communication networks 2021, Vol.2021, p.1-13
Main Authors: Weng, Yu, Chu, Haozhen, Shi, Zhaoyi
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Communication
Computation offloading
Deep learning
Edge computing
Idling
Intelligent vehicles
Learning
Multiagent systems
Nodes
Performance evaluation
Scheduling
User satisfaction
Vehicles
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Intelligent vehicles have provided a variety of services; there is still a great challenge to execute some computing-intensive applications. Edge computing can provide plenty of computing resources for intelligent vehicles, because it offloads complex services from the base station (BS) to the edge computing nodes. Before the selection of the computing node for services, it is necessary to clarify the resource requirement of vehicles, the user mobility, and the situation of the mobile core network; they will affect the users’ quality of experience (QoE). To maximize the QoE, we use multiagent reinforcement learning to build an intelligent offloading system; we divide this goal into two suboptimization problems; they include global node scheduling and independent exploration of agents. We apply the improved Kuhn–Munkres (KM) algorithm to node scheduling and make full use of existing edge computing nodes; meanwhile, we guide intelligent vehicles to the potential areas of idle computing nodes; it can encourage their autonomous exploration. Finally, we make some performance evaluations to illustrate the effectiveness of our constructed system on the simulated dataset.
ISSN:1939-0114
1939-0122
DOI:10.1155/2021/8830879