I wanted to start a discussion about the allocation of "scarce" resources. "Scarce" in this context means resources that are not present on every machine. GPUs are the first example of a scarce resource that we support as a known resource type.
Consider the behavior when there are the following agents in a cluster: 999 agents with (cpus:4,mem:1024,disk:1024) 1 agent with (gpus:1,cpus:4,mem:1024,disk:1024) Here there are 1000 machines but only 1 has GPUs. We call GPUs a "scarce" resource here because they are only present on a small percentage of the machines. We end up with some problematic behavior here with our current allocation model: (1) If a role wishes to use both GPU and non-GPU resources for tasks, consuming 1 GPU will lead DRF to consider the role to have a 100% share of the cluster, since it consumes 100% of the GPUs in the cluster. This framework will then not receive any other offers. (2) Because we do not have revocation yet, if a framework decides to consume the non-GPU resources on a GPU machine, it will prevent the GPU workloads from running! -------- I filed an epic  to track this. The plan for the short-term is to introduce two mechanisms to mitigate these issues: -Introduce a resource fairness exclusion list. This allows the shares of resources like "gpus" to be excluded from the dominant share. -Introduce a GPU_AWARE framework capability. This indicates that the scheduler is aware of GPUs and will schedule tasks accordingly. Old schedulers will not have the capability and will not receive any offers for GPU machines. If a scheduler has the capability, we'll advise that they avoid placing their additional non-GPU workloads on the GPU machines. -------- Longer term, we'll want a more robust way to manage scarce resources. The first thought we had was to have sub-pools of resources based on machine profile and perform fair sharing / quota within each pool. This addresses (1) cleanly, and for (2) the operator needs to explicitly disallow non-GPU frameworks from participating in the GPU pool. Unfortunately, by excluding non-GPU frameworks from the GPU pool we may have a lower level of utilization. In the even longer term, as we add revocation it will be possible to allow a scheduler desiring GPUs to revoke the resources allocated to the non-GPU workloads running on the GPU machines. There are a number of things we need to put in place to support revocation (, , , etc), so I'm glossing over the details here. If anyone has any thoughts or insight in this area, please share! Ben  https://issues.apache.org/jira/browse/MESOS-5377  https://issues.apache.org/jira/browse/MESOS-5524  https://issues.apache.org/jira/browse/MESOS-5527  https://issues.apache.org/jira/browse/MESOS-4392