Running Isolated CPU-Pinned, NUMA-Aligned Workloads and Unpinned Workloads on the Same Hypervisor: A Path Toward Intelligent Infrastructure

Abstract

Modern datacenter infrastructure faces significant challenges when hosting diverse workloads on shared computing resources while maintaining performance guarantees and cost efficiency. This article presents a comprehensive framework for running isolated, CPU-pinned, NUMA-aligned workloads alongside unpinned, flexible workloads on shared hypervisor infrastructure through artificial intelligence-driven optimization techniques. The framework addresses fundamental limitations in current virtualization platforms that fail to exploit NUMA topology information for intelligent workload placement, resulting in performance degradation and resource underutilization. Container-based microservices and telecommunications network functions experience substantial performance penalties from cross-NUMA memory access patterns and unpredictable resource allocation decisions. The proposed solution combines systematic workload classification with machine learning algorithms for predictive placement decisions and dynamic rebalancing capabilities. Implementation involves comprehensive hardware topology discovery, resource partitioning strategies, and integration with existing container orchestration platforms. Performance evaluation demonstrates substantial improvements across telecommunications network functions, edge AI systems, high-performance computing applications, multi-tenant cloud platforms, and future 6G network orchestration scenarios. The framework enables organizations to achieve deterministic performance guarantees for critical applications while maximizing infrastructure utilization through intelligent resource sharing, providing economic benefits through reduced hardware requirements and energy consumption.