We would have the same issue Borries mentioned: the community account user under xsede owns all the jobs. Luckily, Sdsc makes allowances for known gateway, trusting that we represent many individuals. We are building throttling tools to prevent users from submitting more than x running jobs, and placing reserves against their allocation for running jobs.
I don't see how to solve the problem of xsede or other resources seeing a gateway user as equivalent to a regular user without help from xsede policy decisions/infrastructure changes; esp for the case where a code requires a single resource, and is submitted by many users at once. I think solving that would require a resource providers to disambiguate regular and community users. Mark > On Sep 2, 2014, at 10:11 AM, "Borries Demeler" <[email protected]> > wrote: > > Our application involves submission of several hundred quite small (a couple > of minutes for most > clusters, ~128 cores, give or take) computational jobs, running the same code > on multiple datasets. > > We are hitting the limit of 50 jobs on TACC resources, with all others > failing. The problem is > made worse because all users submit under a community account, which treats > every submission to > be part of the same allocation account. > > I see a few possibilities: > > 1. a separate FIFO queue, making sure none of the resources get overloaded by > any community account user > > 2. submitting all jobs as a single job somehow to where the job is submitted > for the aggregate walltime > for all jobs. A special workscript would spawn jobs underneath the parent > submission. Not sure if this > is feasable or reasonable. > > 3. spreading the jobs around all possible resources > > 4. a combination of 1 and 3. > > -Borries > > > > >> On Tue, Sep 02, 2014 at 07:50:12AM -0400, Suresh Marru wrote: >> Hi All, >> >> Need some guidance on identifying a scheduling strategy and a pluggable >> third party implementation for airavata scheduling needs. For context let me >> describe the use cases for scheduling within airavata: >> >> * If we gateway/user is submitting a series of jobs, airavata is currently >> not throttling them and sending them to compute clusters (in a FIFO way). >> Resources enforce per user job limit within a queue and ensure fair use of >> the clusters ((example: stampede allows 50 jobs per user in the normal queue >> [1]). Airavata will need to implement queues and throttle jobs respecting >> the max-job-per-queue limits of a underlying resource queue. >> >> * Current version of Airavata is also not performing job scheduling across >> available computational resources and expecting gateways/users to pick >> resources during experiment launch. Airavata will need to implement >> schedulers which become aware of existing loads on the clusters and spread >> jobs efficiently. The scheduler should be able to get access to heuristics >> on previous executions and current requirements which includes job size >> (number of nodes/cores), memory requirements, wall time estimates and so >> forth. >> >> * As Airavata is mapping multiple individual user jobs into one or more >> community account submissions, it also becomes critical to implement >> fair-share scheduling among these users to ensure fair use of allocations as >> well as allowable queue limits. >> >> Other use cases? >> >> We will greatly appreciate if folks on this list can shed light on >> experiences using schedulers implemented in hadoop, mesos, storm or other >> frameworks outside of their intended use. For instance, hadoop (yarn) >> capacity [2] and fair schedulers [3][4][5] seem to meet the needs of >> airavata. Is it a good idea to attempt to reuse these implementations? Any >> other pluggable third-party alternatives. >> >> Thanks in advance for your time and insights, >> >> Suresh >> >> [1] - >> https://www.tacc.utexas.edu/user-services/user-guides/stampede-user-guide#running >> [2] - >> http://hadoop.apache.org/docs/r2.4.1/hadoop-yarn/hadoop-yarn-site/CapacityScheduler.html >> [3] - >> http://hadoop.apache.org/docs/r2.4.1/hadoop-yarn/hadoop-yarn-site/FairScheduler.html >> [4] - https://issues.apache.org/jira/browse/HADOOP-3746 >> [5] - https://issues.apache.org/jira/browse/YARN-326 >> >>
