When deciding where to schedule your workloads, you have to consider more than just CPU and memory. Whether you are in 5G, AI/ML, HPC, or NFV, you have many more considerations to optimize your workloads. You may care about how busy the node is, how many GPU cards are attached, whether a minimal throughput is available, or whether the node is cooler than the temperature required for basic cooking. Fortunately, Kubernetes allows for extensions to its scheduling paradigm, which allows for new creative solutions going forward. Using these capabilities, we have created a way to use knowledge of your resources to impact your scheduling decisions. Telemetry Aware Scheduling and GPU Aware Scheduling, both open-source projects, enable you to use a variety of metrics in intelligent scheduling. In this talk, we will explain how to deploy and configure your system to handle your varied use cases.
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