Session
AI-Powered Kubernetes Workload Optimization on s390x: Leveraging LLMs for Predictive Scaling
Kubernetes on IBM Z (s390x) presents unique challenges due to hardware constraints (LPARs, shared memory, I/O bottlenecks) and the need for efficient workload placement in Multi-Cloud Platform (MCP) environments. Traditional autoscaling (HPA/VPA) struggles with latency-sensitive workloads (e.g., financial transactions, mainframe-offloaded AI inferencing).
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