Earticle

초록

영어

This study proposes a distributed reinforcement learning system that incorporates the Safe Proper Time (SPT) protocol to address latency issues in cloud-based environments. The system is architected to operate efficiently under limited computational resources, making it suitable for small-scale enterprises. By combining physicallevel technologies such as InfiniBand and TOE with software-level optimization, the SPT protocol enables low-latency, high-throughput data transmission across distributed nodes. Experimental results show that the proposed system reduces response failure rates and achieves faster processing times compared to centralized models. Furthermore, a comparative analysis demonstrates that the system offers competitive advantages over existing machine learning platforms in terms of deployment flexibility and initial cost efficiency. This research contributes to the field by presenting a scalable and resource-efficient approach to distributed reinforcement learning. Future work will focus on enhancing the security and stability of data transmission in SPT-based systems.

목차

Abstract
1. Introduction
2. Related Work
3. Proposed System
3.1 System Overview
3.2 System
3.3 Mitigating Latency Using the SPT Protocol
4. Implementation
5. Conclusion
References

키워드

저자

• Jong-Sub Lee [ Professor, College of General Education, Semyung University, Jecheon, Korea ]
• Seok-Jae Moon [ Professor, Department of Artificial Intelligence Institute of Information Technology, 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