Artificial Intelligence for Autonomous Infrastructure: A Deep Reinforcement Learning Approach to Datacenter Operations

Ashok Mohan Chowdhary Jonnalagadda

doi:10.15662/gn84s304

Authors

Ashok Mohan Chowdhary Jonnalagadda Hilmar, USA Author

DOI:

https://doi.org/10.15662/gn84s304

Keywords:

Deep Reinforcement Learning, Intelligent Datacenter Management, Artificial Intelligence for Infrastructure Management, Energy-Efficient Cloud Computing, Self-Optimizing Control Systems

Abstract

The current datacenter operations are more complex than ever before due to the skyrocketing demand for cloud services, Internet of Things (IoT) applications, and real-time analytics. Classical rule-of-thumb control and heuristic optimization cannot keep up with the highly dynamic nature of non-linear large-scale computing infrastructure. The paper explores deep reinforcement learning (DRL) as a basis for fully autonomous infrastructure management, specifically thermal regulation, workload scheduling, and energy-conscious resource allocation.

We initially examine the shortcomings of traditional datacenter control loops and outline the gaps that do not facilitate scalability and fault tolerance. Our next suggestion is a hybrid DARA system comprising model-free policy learning and predictive simulations of digital twins to allow self-optimizing behavior under unpredictable workloads and equipment breakdowns. An implementation on a simple datacenter simulator using live telemetry streams has been tested and shown to perform 18 percent better in cooling energy and 12 percent better in resource utilization than state-of-the-art baselines.

The findings attest to the fact that DRL can assist in autonomous infrastructure that is capable of constant adaptation without human assistance. We mention the practical deployment issues, such as data quality, safety limitations, and how it works with the legacy orchestration platforms, and the future research directions that would bring us to the fully self-governing datacenters. The study also adds to the existing literature that AI-based control can reduce the operational expenses and environmental footprint significantly and enhance the reliability of the provided services.

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