Optimizing the Efficiency of Computational Platforms: Traditional vs. Containerized Setups

Optimizing computational platforms in digital labs is vital for education, research, and enterprise applications. Traditional setups face resource inefficiencies, scalability constraints, and configuration conflicts, hindering performance. This paper compares traditional and containerized setups using Docker and Kubernetes, evaluating metrics such as CPU usage, memory consumption, latency, and setup time. Results reveal containerization reduces CPU usage by 23%, memory by 34%, latency by 40%, and setup time by 80%, while improving scalability by 140%. Proxy containers and orchestration effectively resolve MySQL plugin mismatches, ensuring uninterrupted operation and efficient resource utilization. These findings underscore containerization’s role in enhancing digital lab efficiency, scalability, and adaptability. Future research will focus on AI-driven orchestration techniques and advanced network policies to address challenges in multi-container environments.