Climate-Conscious Computing: Designing Sustainable Data Centers
Abstract
Data centers are essential to the digital world but contribute significantly to global energy consumption. This paper investigates the development of climate-conscious data centers through innovations in energy-efficient architecture, renewable-powered cooling systems, and AI-based resource allocation. We present comparative studies on green data centers using solar and geothermal energy, and explore techniques like server virtualization and liquid cooling. The research emphasizes strategies to minimize carbon emissions while ensuring high-performance computing.
References
Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798-1828.
Gu, S., Kelly, B., & Xiu, D. (2018). Empirical Asset Pricing via Machine Learning. National Bureau of Economic Research, Working Paper No. 25398.
Kroll, J. A., Huey, J., Barocas, S., Felten, E. W., Reidenberg, J. R., Robinson, D. G., & Yu, H. (2017). Accountable Algorithms. University of Pennsylvania Law Review, 165(3), 633-705.
Narayanan, A., & Shmatikov, V. (2010). Myths and fallacies of "personally identifiable information". Communications of the ACM, 53(6), 24-26.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
Chittineni, S. (2019). Optimizing Microservices Performance with Reinforcement Learning: A Case Study in Spring Boot Applications. American Journal of AI & Innovation, 1(1).
Chittineni, S. (2019). AI-Driven Optimization of Backend Systems: Enhancing Performance and Reducing Latency in Large-Scale Applications. Australian Journal of Cross-Disciplinary Innovation, 1(1).
Mohammed, C. S. A. (2019). Exploring the Features and Scope of SAP S/4HANA for Financial Products Subledger Management. Australian Journal of Cross-Disciplinary Innovation, 1(1).
Chittineni, S. (2020). Leveraging Machine Learning for Automated Thread Dump Analysis and Performance Tuning in Enterprise Java Applications. Australian Journal of Cross-Disciplinary Innovation, 2(2).