[slurm-users] Large job starvation on cloud cluster
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
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
> 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
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