강화학습 기반의 CBDC 처리량 및 네트워크 부하 문제 해결 기술
Enhancing Throughput and Reducing Network Load in Central Bank Digital Currency Systems using Reinforcement Learning
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초록
디지털 전환이 다양한 분야에서 가속되고 있는 가운데, 금융시장에서도 디지털·전자화된 화폐를 포함한 지급결제수단 발전에 관한 관심이 집중되고 있다. 그중 중앙은행 디지털화폐(Central Bank Digital Currency, CBDC)는 기존 실물 화폐를 대체할 수 있는 미래 디지털화폐로 가치변동이 없으며 기존 실물 화폐인 현금과 1:1 등가교환이 가능하다. 최근 국내·외에서는 CBDC 출시를 위해 다양한 연구 및 개발을 진행하고 있다. 그러나, 현재 CBDC 시스템은 대용량 거래에 대한 처리 속도 지연, 응답대기시간 지연 및 네트워크 부하 등 CBDC 확장성에 관한 문제가 존재한다. 범용적인 CBDC 시스템을 구축하기 위해서는 기존 블록체인의 낮은 처리량 및 네트워크 부하 문제 등의 확장성 문제를 해결해야 한다. 따라서, 본 연구에서는 범용 CBDC 구축을 위한 강화학습 기반의 CBDC 환경에서 대용량 데이터에 대한 처리량 및 네트워크 부하 문제 해결 기술을 제안한다. 제안 기술은 기존 시스템 대비 최대 64배 이상의 처리량 증대 및 20% 이상의 네트워크 부하를 감소할 수 있다.
Abstract
Amidst the acceleration of digital transformation across various sectors, the financial market is increasingly focusing on the development of digital and electronic payment methods, including currency. Among these, Central Bank Digital Currencies (CBDC) are emerging as future digital currencies that could replace physical cash. They are stable, not subject to value fluctuation, and can be exchanged one-to-one with existing physical currencies. Recently, both domestic and international efforts are underway in researching and developing CBDCs. However, current CBDC systems face scalability issues such as delays in processing large transactions, response times, and network congestion. To build a universal CBDC system, it is crucial to resolve these scalability issues, including the low throughput and network overload problems inherent in existing blockchain technologies. Therefore, this study proposes a solution based on reinforcement learning for handling large-scale data in a CBDC environment, aiming to improve throughput and reduce network congestion. The proposed technology can increase throughput by more than 64 times and reduce network congestion by over 20% compared to existing systems.
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