[slurm-users] Large job starvation on cloud cluster

Andy Riebs andy.riebs at hpe.com
Wed Feb 27 20:38:58 UTC 2019

Michael, are you setting time limits for the jobs? That's a huge part of 
a scheduler's decision about whether another job can be run. For 
example, if a job is running with the Slurm default of "infinite," the 
scheduler will likely decide that jobs that will fit in the remaining 
nodes will be able to finish before the job that requires infinite time.


*From:* Michael Gutteridge <michael.gutteridge at gmail.com>
*Sent:* Wednesday, February 27, 2019 3:29PM
*To:* Slurm User Community List <slurm-users at lists.schedmd.com>
*Subject:* [slurm-users] Large job starvation on cloud cluster
I've run into a problem with a cluster we've got in a cloud provider- 
hoping someone might have some advice.

The problem is that I've got a circumstance where large jobs _never_ 
start... or more correctly, that large-er jobs don't start when there 
are many smaller jobs in the partition.  In this cluster, accounts are 
limited to 300 cores.  One user has submitted a couple thousand jobs 
that each use 6 cores.  These queue up, start nodes, and eventually all 
300 cores in the limit are busy and the remaining jobs are held with 
"AssocGrpCpuLimit".  All as expected.

Then another user submits a job requesting 16 cores.  This one, too, 
gets held with the same reason.  However, that larger job never starts 
even if it has the highest priority of jobs in this account (I've set it 
manually and by using nice).

What I see in the sched.log is:

sched: [2019-02-25T16:00:14.940] Running job scheduler
sched: [2019-02-25T16:00:14.941] JobId=2210784 delayed for accounting policy
sched: [2019-02-25T16:00:14.942] JobId=2203130 initiated
sched: [2019-02-25T16:00:14.942] Allocate JobId=2203130 NodeList=node1 
#CPUs=6 Partition=largenode

In this case, 2210784 is the job requesting 16 cores and 2203130 is one 
of the six core jobs.  This seems to happen with either the backfill or 
builtin scheduler.  I suspect what's happening is that when one of the 
smaller jobs completes, the scheduler first looks at the higher-priority 
large job, determines that it cannot run because of the constraint, 
looks at the next job in the list, determines that it can run without 
exceeding the limit, and then starts that job.  In this way, the larger 
job isn't started until all of these smaller jobs complete.

I thought that switching to the builtin scheduler would fix this, but as 
slurm.conf(5) indicates:

 > An exception is made for jobs that can not run due
> to  partition constraints (e.g. the time limit) or
>  down/drained nodes.  In that case, lower priority
> jobs can  be initiated and not impact the higher
> priority  job.

I suspect one of these exceptions is being triggered- the limit is in 
the job's association, so I don't think it's a partition constraint.  I 
don't have this problem with the on-premises cluster, so I suspect it's 
something to do with power management and the state of powered-down nodes.

I've sort-of worked around this by setting a per-user limit lower than 
the per-account limit, but that doesn't address any situation where a 
single user submits large and small jobs and does lead to some other 
problems in other groups, so it's not a long-term solution.

Thanks for having a look

  - Michael

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