基于计算工程的人工智能和机器学习驱动的鲁棒数据中心安全管理方法

Senthilkumar G, Rajendran P, Suresh Y, Herald Anantha Rufus N, Rama chaithanya Tanguturi, Rajdeep Singh Solanki
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引用次数: 0

摘要

本研究探讨了人工智能(AI),特别是递归神经网络(RNN)模型,通过计算工程整合到数据中心冷却系统的优化中。该研究利用计算流体动力学(CFD)模拟作为基础数据源,旨在通过预测建模提高数据中心的运营效率和可持续性。研究结果表明,经过CFD数据集训练的RNN模型可以熟练地预测数据中心的关键条件,包括温度变化和气流动力学。与传统方法相比,这种人工智能驱动的方法具有明显的优势,可以显著减少因过冷而导致的能源浪费。此外,该模型的主动特性允许及时识别和缓解潜在的设备挑战或热热点,确保不间断运行和设备寿命。虽然该研究展示了将人工智能与数据中心运营相结合的变革潜力,但它也指出了进一步完善的领域,包括模型对各种现实场景的适应性以及对长期依赖关系的管理。总之,该研究阐明了增强数据中心运营的一条有前途的途径,强调了人工智能驱动方法在实现效率、降低成本和环境可持续性方面的显著优势。
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Computational Engineering based approach on Artificial Intelligence and Machine learning-Driven Robust Data Centre for Safe Management
This research explores the integration of Artificial Intelligence (AI), specifically the Recurrent Neural Network (RNN) model, into the optimization of data center cooling systems through Computational Engineering. Utilizing Computational Fluid Dynamics (CFD) simulations as a foundational data source, the study aimed to enhance operational efficiency and sustainability in data centers through predictive modeling. The findings revealed that the RNN model, trained on CFD datasets, proficiently forecasted key data center conditions, including temperature variations and airflow dynamics. This AI-driven approach demonstrated marked advantages over traditional methods, significantly minimizing energy wastage commonly incurred through overcooling. Additionally, the proactive nature of the model allowed for the timely identification and mitigation of potential equipment challenges or heat hotspots, ensuring uninterrupted operations and equipment longevity. While the research showcased the transformative potential of merging AI with data center operations, it also indicated areas for further refinement, including the model's adaptability to diverse real-world scenarios and its management of long-term dependencies. In conclusion, the study illuminates a promising avenue for enhancing data center operations, highlighting the significant benefits of an AI-driven approach in achieving efficiency, cost reduction, and environmental sustainability.
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