Energy Efficient Placement of Edge Workloads - Parul Singh & Kaiyi Liu, Red Hat
Channel: CNCF [Cloud Native Computing Foundation]
Date: 28/10/2022

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Energy Efficient Placement of Edge Workloads - Parul Singh & Kaiyi Liu, Red Hat

Currently, the energy consumption metrics are only available at node levels. There is no way to obtain container-level energy consumption. Autoscalers and schedulers really need pod-level metrics data in order to obtain energy savings from resizing or migrating containers. The presentation introduces Kubernetes-based Efficient Power Level Exporter (Kepler) and its integration with Kubernetes. By leveraging eBPF programs, Kepler probes per container energy consumption related system counters and exports them as metrics. These metrics help end users observe their containers’ energy consumption and allow cluster admins to make intelligent decisions on achieving energy conservation goals. The next part of the presentation shows that Kepler can be easily integrated into Prometheus and render time series metrics into Grafana. At last, we will demonstrate sustainable management of clusters by leveraging Kepler, Cloud Native patterns, Observability and Kubernetes features like node selector, node labels, node name, affinity and anti-affinity to achieve energy efficient placement of edge workloads based on the ideal load of an edge destination.