Scenario 1: Resource Allocation
For this first scenario, deploy this app definition as follows:
$ dcos marathon app add https://raw.githubusercontent.com/dcos-labs/dcos-debugging/master/1.10/app-scaling1.json
Check the application status using the DC/OS GUI, you should see something like the following:
Figure 1. DC/OS GUI showing app status
with the status of the application most likely to be “Waiting” followed by some number of thousandths “x/1000”. “Waiting” refers to the overall application status and the number; “x” here represents how many instances have successfully deployed (6 in this example).
You can also check this status from the CLI:
dcos marathon app list
which produces the following output in response:
ID MEM CPUS TASKS HEALTH DEPLOYMENT WAITING CONTAINER CMD /app-scaling-1 128 1 6/1000 --- scale True mesos sleep 10000
Or, if you want to see all ongoing deployments, enter:
dcos marathon deployment list
to see something like the following:
APP POD ACTION PROGRESS ID /app-scaling-1 - scale 1/2 c51af187-dd74-4321-bb38-49e6d224f4c8
So now we know that some (6/1000) instances of the application have successfully deployed, but the overall deployment status is “Waiting”. But what does this mean?
The “Waiting” state means that DC/OS (or more precisely Marathon) is waiting for a suitable resource offer. So it seems to be an deployment issue and we should start by checking the available resources.
If we look at the DC/OS dashboard, we should see a pretty high CPU allocation similar to the following (of course, the exact percentage depends on your cluster):
Figure 2. DC/OS resource allocation display
Since we are not yet at 100% allocation, but we are still waiting to deploy, something interesting is going on. So let’s look at the recent resource offers in the debug view of the DC/OS GUI.
Figure 3. Recent resource offers
We can see that there are no matching CPU resources. But again, the overall CPU allocation is only at 75%. Further puzzling, when we take a look at the ‘Details’ section further below, we see that the latest offers from a different host match the resource requirements of our application. So, for example, the first offer coming from host
10.0.0.96 matched the role, constraint (not present in this
app-definition) memory, disk, port resource requirements — but failed the CPU resource requirements. The offer before this also seemed like it should have met the resource requirements. So despite it looking like we have enough CPU resources available, the application seems to be failing for just this reason.
Let’s look at the details more closely.
Figure 4. Rsource allocation details
According to this, some of the remaining CPU resources are allocated to a different Mesos resource role and so cannot be used by our application (it runs in role ‘*’, the default role).
To check the roles of different resources let us have a look at the state-summary endpoint, which you can access at
That endpoint will give us a rather long JSON output, so it is helpful to use jq to make the output readable:
curl -skSL -X GET -H "Authorization: token=$(dcos config show core.dcos_acs_token)" -H "Content-Type: application/json" "$(dcos config show core.dcos_url)/mesos/state-summary" | jq '.'
When looking at the agent information we can see two different kinds of agent.
Figure 5. Cluster information
The first kind has no free CPU resources and also no reserved resources. Of course, this might be different if you had other workloads running on your cluster prior to these exercises. Note that these unreserved resources correspond to the default role ‘*’ — the role by which we are trying to deploy our tasks.
The second kind has unused CPU resources, but these resources are reserved in the role ‘slave_public’.
We now know that the issue is that there are not enough resources in the desired resource role across the entire cluster. As a solution we could either scale down the application (1000 instances does seem a bit excessive), or we need to add more resources to the cluster.
This was a straightforward scenario with too few CPU resources. Typically resource issues are more likely caused by more complex factors - such as improperly configured port resources or placement constraints. Nonetheless, this general workflow pattern still applies.
Remove the application from the cluster with:
dcos marathon app remove /app-scaling-1