[openstack-dev] Scheduler proposal
clint at fewbar.com
Sat Oct 10 01:29:27 UTC 2015
Excerpts from Chris Friesen's message of 2015-10-09 17:33:38 -0700:
> On 10/09/2015 03:36 PM, Ian Wells wrote:
> > On 9 October 2015 at 12:50, Chris Friesen <chris.friesen at windriver.com
> > <mailto:chris.friesen at windriver.com>> wrote:
> > Has anybody looked at why 1 instance is too slow and what it would take to
> > make 1 scheduler instance work fast enough? This does not preclude the
> > use of
> > concurrency for finer grain tasks in the background.
> > Currently we pull data on all (!) of the compute nodes out of the database
> > via a series of RPC calls, then evaluate the various filters in python code.
> > I'll say again: the database seems to me to be the problem here. Not to
> > mention, you've just explained that they are in practice holding all the data in
> > memory in order to do the work so the benefit we're getting here is really a
> > N-to-1-to-M pattern with a DB in the middle (the store-to-DB is rather
> > secondary, in fact), and that without incremental updates to the receivers.
> I don't see any reason why you couldn't have an in-memory scheduler.
> Currently the database serves as the persistant storage for the resource usage,
> so if we take it out of the picture I imagine you'd want to have some way of
> querying the compute nodes for their current state when the scheduler first
> starts up.
> I think the current code uses the fact that objects are remotable via the
> conductor, so changing that to do explicit posts to a known scheduler topic
> would take some work.
Funny enough, I think thats exactly what Josh's "just use Zookeeper"
message is about. Except in memory, it is "in an observable storage
Instead of having the scheduler do all of the compute node inspection
and querying though, you have the nodes push their stats into something
like Zookeeper or consul, and then have schedulers watch those stats
for changes to keep their in-memory version of the data up to date. So
when you bring a new one online, you don't have to query all the nodes,
you just scrape the data store, which all of these stores (etcd, consul,
ZK) are built to support atomically querying and watching at the same
time, so you can have a reasonable expectation of correctness.
Even if you figured out how to make the in-memory scheduler crazy fast,
There's still value in concurrency for other reasons. No matter how
fast you make the scheduler, you'll be slave to the response time of
a single scheduling request. If you take 1ms to schedule each node
(including just reading the request and pushing out your scheduling
result!) you will never achieve greater than 1000/s. 1ms is way lower
than it's going to take just to shove a tiny message into RabbitMQ or
even 0mq. So I'm pretty sure this is o-k for small clouds, but would be
a disaster for a large, busy cloud.
If, however, you can have 20 schedulers that all take 10ms on average,
and have the occasional lock contention for a resource counter resulting
in 100ms, now you're at 2000/s minus the lock contention rate. This
strategy would scale better with the number of compute nodes, since
more nodes means more distinct locks, so you can scale out the number
of running servers separate from the number of scheduling requests.
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