Earticle

초록

영어

Reinforcement learning is a technology that can present successful and creative solutions in many areas. This reinforcement learning technology was used to deploy containers from cloud servers to fog servers to help them learn the maximization of rewards due to reduced traffic. Leveraging reinforcement learning is aimed at predicting traffic in the network and optimizing traffic-based fog computing network environment for cloud, fog and clients. The reinforcement learning system collects network traffic data from the fog server and IoT. Reinforcement learning neural networks, which use collected traffic data as input values, can consist of Long Short-Term Memory (LSTM) neural networks in network environments that support fog computing, to learn time series data and to predict optimized traffic. Description of the input and output values of the traffic-based reinforcement learning LSTM neural network, the composition of the node, the activation function and error function of the hidden layer, the overfitting method, and the optimization algorithm.

목차

Abstract
1. Introduction
2. Related Work
2.1 Fog Computing
2.2 Reinforcement Learning
2.3 LSTM Neural Network
3. Reinforcement learning with neural network algorithm
3.1 Fog computing container deployment system
3.2 Design of reinforcement learning policy learner
3.3 Design of policy neural network
3.4 Results of system application
4. Conclusion
References

키워드

저자

• Tae-Won Jung [ Researcher, Graduate School of Smart Convergence, KWANGWOON University, Korea ]
• Jong-Yong Lee [ Professor, Ingenium college of liberal arts, KWANGWOON University, Korea ]
• Kye-Dong Jung [ Professor, Ingenium college of liberal arts, KWANGWOON University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380