Kubernetes has taken the world by storm attracting unconventional workloads such as HPC Edge, IoT, Telco and Comm service providers, 5G, AI/ML and NFV solutions to it. This talk would benefit users, engineers, and cluster admins deploying performance sensitive workloads on k8s. Addition of newer nodes running alongside older ones in data centers results in hardware heterogeneity. Motivated by saving physical space in the data centers, newer nodes are packed with more CPUs, enhanced hardware capabilities. Exposing to use fine grain topology information for optimised workload placement would help service providers and VNF vendors too. We’ll explain numerous challenges encountered in efficiently deploying workloads due to inability to understand the hardware topology of the underlying bare metal infrastructure and scheduling based on it. Scheduler’s lack of knowledge of resource topology can lead to unpredictable application performance, in general under-performance, and in the worst case, complete mismatch of resource requests and kubelet policies, scheduling a pod where it is destined to fail, potentially entering a failure loop. Exposing cluster level topology to the scheduler empowers it to make intelligent NUMA aware placement decisions optimizing cluster wide performance of workloads. This would benefit Telco User Group in kubernetes, kubernetes and the overall CNCF ecosystem enabling improved application performance without impacting user experience.