Clint Byrum wrote:
Excerpts from Ed Leafe's message of 2015-10-15 11:56:24 -0700:
Wow, I seem to have unleashed a bunch of pent-up frustration in the
community! It's great to see everyone coming forward with their
ideas and insights for improving the way Nova (and, by extension,
all of OpenStack) can potentially scale.
I do have a few comments on the discussion:
1) This isn't a proposal to simply add some sort of DLM to Nova as
a magic cure-all. The concerns about Nova's ability to scale have
to do a lot more with the overall internal communication design.
In this, we agree.
2) I really liked the comment about "made-up numbers". It's so
true: we are all impressed by such examples of speed that we
sometimes forget whether speeding up X will improve the overall
process to any significant degree. The purpose of my original email
back in July, and the question I asked at the Nova midcycle, is if
we could get some numbers that would be a target to shoot for with
any of these experiments. Sure, I could come up with a test that
shows a zillion transactions per second, but if that doesn't result
in a cloud being able to schedule more efficiently, what's the
point?
Speed is only 1 dimension. Efficiency and simplicity are two others
that I think are harder to quantify, but are also equally important
in any component of OpenStack.
3) I like the idea of something like ZooKeeper, but my concern is
how to efficiently query the data. If, for example, we had records
for 100K compute nodes, would it be possible to do the equivalent
of "SELECT * FROM resources WHERE resource_type = 'compute' AND
free_ram_mb>= 2048 AND …" - well, you get the idea. Are complex
data queries possible in ZK? I haven't been able to find that
information anywhere.
You don't do complex queries, because you have all of the data in
RAM, in an efficient in-RAM format. Even if each record is 50KB, we
can do 100,000 of them in 5GB. That's a drop in the bucket.
4) It is true that even in a very large deployment, it is possible
to keep all the relevant data needed for scheduling in memory. My
concern is how to efficiently search that data, much like in the ZK
scenario.
There are a bunch of ways to do this. My favorite is to have filter
plugins in the scheduler define what they need to index, and then
build a B-tree for each filter as each record arrives in the main
data structure. When scheduling requests come in, they simply walk
through each B-tree and turn that into a set. Then read each piece of
the set out of the main structure and sort based on whichever you
want (less full for load balancing, most full for efficient
stacking).
Another idea is to use numpy and start representing filters as linear
equations, then use something like
https://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.solve.html#numpy.linalg.solve
to solve linear equations given some data.
Another idea, turn each filter into a constraint equation (which it
sorta is anyway) and use a known fast constraint solver on that data...
Lots of ideas here that can be possible, likely endless :)
5) Concerns about Cassandra running with OpenJDK instead of the
Oracle JVM are troubling. I sent an email about this to one of the
people I know at DataStax, but so far have not received a response.
And while it would be great to have people contribute to OpenJDK to
make it compatible, keep in mind that that would be an ongoing
commitment, not just a one-time effort.
There are a few avenues to success with Cassandra but I don't think
any of them pass very close to OpenStack's current neighborhood.
6) I remember discussions back in the Austin-Bexar time frame about
what Thierry referred to as 'flavor-based schedulers', and they
were immediately discounted as not sophisticated enough to handle
the sort of complex scheduling requests that were expected. I'd be
interested in finding out from the big cloud providers what
percentage of their requests would fall into this simple structure,
and what percent are more complicated than that. Having hosts
listening to queues that they know they can satisfy removes the
raciness from the process, although it would require some
additional handling for the situation where no host accepts the
request. Still, it has the advantage of being dead simple.
Unfortunately, this would probably require a bigger architectural
change than integrating Cassandra into the Scheduler would.
No host accepting the request means your cloud is, more or less,
full. If you have flavors that aren't proper factors of smaller
flavors, this will indeed happen even when it isn't 100% utilized. If
you have other constraints that you allow your users to specify, then
you are letting them dictate how your hardware is utilized, which I
think is a foolhardy business decision. This is no different than any
other manufacturing batch size problem: sometimes parts of your
process are under utilized, and you have to make choices about
rejecting certain workloads if they will end up costing you more than
you're willing to pay for the happy customer.
Note that the "efficient stacking" model I talked about can't really
work in the queue-based approach. If you want to fill up the most
full hosts before filling more, you need some awareness of what host
is most full and the compute nodes can't really know that.
I hope that those of us who will be at the Tokyo Summit and are
interested in these ideas can get together for an informal
discussion, and come up with some ideas for grand experiments and
reality checks. ;-)
BTW, I started playing around with some ideas, and thought that if
anyone wanted to also try Cassandra, I'd write up a quick how-to
for setting up a small cluster:
http://blog.leafe.com/small-scale-cassandra/. Using docker images
makes it a breeze!
Really cool Ed. I agree, we need a barcamp just for scheduler ideas.
:)
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