| ABSTRACT |
This paper presents a comprehensive systemslevel framework for designing cloud infrastructures
purposebuilt to support modern artificial intelligence (AI) workloads. As AI models become increasingly complex, data
and computeintensive, traditional cloud architectures struggle to meet performance, cost, and scalability demands. We
propose an AIoptimized infrastructure model that integrates heterogeneous computing (GPUs, TPUs, custom
accelerators), tiered storage (inmemory, NVMe, object store), dynamic orchestration, and resource pooling via
infrastructureascode. Central to our approach is a feedback loop incorporating realtime telemetry and workload
profiling to drive autoscaling, resource scheduling, data locality optimization, and energy efficiency. We evaluate our
design through a mixed?methodology combining simulation benchmarks, small?scale cloud deployments, and
costperformance modeling. Results show significant improvements over baseline generalpurpose cloud setups: up to 3×
reduction in training time, 40% lower cost for inference workloads, and improved resource utilization density (by
50%). We discuss tradeoffs in hardware heterogeneity, orchestration complexity, monitoring overhead, and developer
adoption challenges. Finally, we outline best practices for integrating AI workload profiling into CI/CD pipelines,
infrastructureascode templates for AI clusters, and emergent directions in serverless GPU autoscaling and
edgeintegrated AI cloud. Keywords include AI infrastructure, heterogeneous computing, cloud orchestration, resource
optimization, telemetry, infrastructureascode. This systemsoriented research bridges theory and practice, offering a
blueprint for both practitioners and architects seeking to align cloud infrastructure with the evolving demands of AI
development and deployment. |
