Composable Intelligence: AI-Driven Resource Fabric Optimization on Cisco UCSX for Next-Gen Enterprise Workloads

Modern enterprises face unprecedented infrastructure challenges due to the explosive growth of heterogeneous workloads like AI/ML training, real-time analytics, and GPU-accelerated applications. Traditional static resource allocation strategies lead to severe underutilization (often <40%) and performance bottlenecks. This paper introduces Composable Intelligence—an AI-driven framework for autonomous optimization of compute, memory, and GPU resources in Cisco UCSX composable infrastructure. Leveraging a multi-agent Deep Reinforcement Learning (DRL) system integrated with Graph Neural Networks (GNNs), our solution dynamically reconfigures hardware resources at sub-second latency based on real-time telemetry and predictive analytics. Implemented on a UCSX 210c-M7 testbed with 32 blades and 16 NVIDIA A100 GPUs, Composable Intelligence achieved 92.7% average resource utilization while reducing SLA violations by 83% and energy consumption by 22% compared to threshold-based orchestration. The framework’s policy-driven actuation engine executes hardware reconfiguration via Cisco UCSX Manager APIs within 650ms, enabling true workload-adaptive infrastructure.