Authors: Uma Perumal, Vasantharajan Renganathan
Abstract: In this paper, we introduce a novel mathematical approach to connect fluid dynamics with artificial intelligence and cloud computing optimization. We introduce the AI-Cloud-Navier- Stokes (ACNS) System, a new framework which treats cloud infrastructure as continuum fluid system and allows us to predictably optimize resource deployments, load balance and failure tolerant. By correspondence typical cloud computing variables and fluid dynamic quantities – workload being mapped to velocity, resource availability to pressure, as the network latency to viscosity – we obtain a full set of partial differential equations enriched with neural network operators. The main technical contributions of our work are: (1) the formulation of a new set of cloud-specific Navier-Stokes equations, derived through rigorous mathematical theory; (2) a proof on AI-enhanced singularity prevention for stable systems; and (3) an applied case study which shows 23-28% reduction in cost and 85% accuracy in failure prediction from real-world cloud data. Beyond theoretical novelty, we evaluate the system empirically for large-scale computational problems based on production cloud data from AWS EC2 instances, and find that it significantly outperforms traditional optimization techniques. This work paves the way for a new direction of physics-informed AI for distributed systems optimization, with applications ranging from edge computing to IoT networks and large-scale datacentre orchestration.
