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From "Robert Joseph Evans (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (YARN-371) Resource-centric compression in AM-RM protocol limits scheduling
Date Tue, 05 Feb 2013 21:11:14 GMT

    [ https://issues.apache.org/jira/browse/YARN-371?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13571731#comment-13571731

Robert Joseph Evans commented on YARN-371:

If you want to move this to yarn-dev@ that is fine with me.  My idea was not to support both
protocols, but to support different in memory layouts.  There would just be the single task
centric protocol, assuming that it is not too expensive, that the scheduler could then decided
how it wanted to store the requests.  If it threw away the task centric nature and compressed
it to be node centric then that is the decision of the scheduler, but it does not force other
schedulers to do the same thing.

That is why I wanted the table.  I want to know what the overhead is going to be compared
to what it is now. I don't think it is going to be that bad, because we are already sending
the responses back in a container centric way, and so are all of the heartbeats from the node
managers.  Now granted all of those are limited by the size of the cluster.  

Just back of the envelope estimates don't seem that horrible, not great but also not horrible.
 Please correct my arithmetic if you see anything a miss, but 100,000 tasks at 150 bytes each
(name of 3 nodes plus some extra stuff) is going to take about 15MB to transfer. To saturate
a gigbit Ethernet connection would in theory take on the order of 6.5 million task requests/sec
in the real world along with other traffic we probably would not want to go over 1 million
task requests/sec.  And because the protocol is a delta protocol, just an assumption based
off of how it currently works, that would be 1 million new task requests/sec or about 10 very
large jobs being launched a second. If each of those containers were on average about 1 GB
it would take a cluster with a churn of 1 PB/second to have the sustained 1 million requests/second.
Or about 250 containers finishing every second on every node in a 4000 node cluster.

If we move to 10GigE then there really is nothing to worry about from a networking perspective.

The next question after that would be how difficult is it to do this transformation.  Is it
going to become a CPU bottleneck, or is the memory usage while the transformation is happening
too high.  Well that is something that would really require some profiling to be sure what
the impact is.  From what I see it seems like a reasonable change to create a quick prototype
of, and see how it performs.
> Resource-centric compression in AM-RM protocol limits scheduling
> ----------------------------------------------------------------
>                 Key: YARN-371
>                 URL: https://issues.apache.org/jira/browse/YARN-371
>             Project: Hadoop YARN
>          Issue Type: Improvement
>          Components: api, resourcemanager, scheduler
>    Affects Versions: 2.0.2-alpha
>            Reporter: Sandy Ryza
>            Assignee: Sandy Ryza
> Each AMRM heartbeat consists of a list of resource requests. Currently, each resource
request consists of a container count, a resource vector, and a location, which may be a node,
a rack, or "*". When an application wishes to request a task run in multiple localtions, it
must issue a request for each location.  This means that for a node-local task, it must issue
three requests, one at the node-level, one at the rack-level, and one with * (any). These
requests are not linked with each other, so when a container is allocated for one of them,
the RM has no way of knowing which others to get rid of. When a node-local container is allocated,
this is handled by decrementing the number of requests on that node's rack and in *. But when
the scheduler allocates a task with a node-local request on its rack, the request on the node
is left there.  This can cause delay-scheduling to try to assign a container on a node that
nobody cares about anymore.
> Additionally, unless I am missing something, the current model does not allow requests
for containers only on a specific node or specific rack. While this is not a use case for
MapReduce currently, it is conceivable that it might be something useful to support in the
future, for example to schedule long-running services that persist state in a particular location,
or for applications that generally care less about latency than data-locality.
> Lastly, the ability to understand which requests are for the same task will possibly
allow future schedulers to make more intelligent scheduling decisions, as well as permit a
more exact understanding of request load.
> I would propose the tweak of allowing a single ResourceRequest to encapsulate all the
location information for a task.  So instead of just a single location, a ResourceRequest
would contain an array of locations, including nodes that it would be happy with, racks that
it would be happy with, and possibly *.  Side effects of this change would be a reduction
in the amount of data that needs to be transferred in a heartbeat, as well in as the RM's
memory footprint, becaused what used to be different requests for the same task are now able
to share some common data.
> While this change breaks compatibility, if it is going to happen, it makes sense to do
it now, before YARN becomes beta.

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