Abstract: This paper proposes a remote experiment platform based on hyper converged infrastructure and virtual private network technologies. The platform addresses limitations of traditional systems in resource allocation, data storage, and network stability. Key features include distributed computing, software-defined storage, high-availability clustering, and intelligent load balancing, which together improve reliability and resource efficiency. A Markov chain is used to model fault recovery, and the M/G/1 queuing model is applied to optimize node server load. Experimental results show reduced data access latency, shorter fault recovery time, and improved concurrency compared with conventional architectures. Teaching practice confirms the platform’s effectiveness and its potential for application in remote education.