Statistical and Probabilistic Optimization of Data Centre Capacity Component Power Redundancy

Abstract:
Power redundancy plays a crucial role in ensuring continuous operation in data centers, a rapidly growing industry. Redundant capacity components are essential to prevent power failures in data centers, yet over-provisioning can lead to increased capital expenses, energy waste, and environmental impacts. By leveraging statistical models, Monte Carlo simulations, and Bayesian networks, our method enables data centers to determine the optimal number of redundant capacity components based on failure probability and performance data. This paper presents a statistical and probabilistic optimization approach to enhance power redundancy in data center power supplies, balancing reliability, cost, and efficiency. The proposed optimization framework includes predictive maintenance scheduling, which proactively addresses potential failures, further reducing downtime risks and extending the lifespan of power supply components. The findings indicate that data centers adopting optimized redundancy configurations can significantly reduce costs and energy consumption while maintaining high operational reliability. The approach also offers scalability, allowing data centers to adjust redundancy as power demands grow, making it a sustainable solution for evolving infrastructure needs. This study provides valuable insights into redundancy optimization as a strategic tool for resilient and cost-effective power management in data centers, with potential applications in other critical infrastructure systems.