On 10/10/15, 11:35 PM, "Clint Byrum"<cl...@fewbar.com> wrote:
Excerpts from Alec Hothan (ahothan)'s message of 2015-10-09 21:19:14 -0700:
On 10/9/15, 6:29 PM, "Clint Byrum"<cl...@fewbar.com> wrote:
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.frie...@windriver.com
<mailto:chris.frie...@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
location".
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.
That is not what I have seen, measurements that I did or done by others show
between 5000 and 10000 send *per sec* (depending on mirroring, up to 1KB msg
size) using oslo messaging/kombu over rabbitMQ.
You're quoting througput of RabbitMQ, but how many threads were
involved? An in-memory scheduler that was multi-threaded would need to
implement synchronization at a fairly granular level to use the same
in-memory store, and we're right back to the extreme need for efficient
concurrency in the design, though with much better latency on the
synchronization.
These were single-threaded tests and you're correct that if you had multiple
threads trying to send something you'd have some inefficiency.
However I'd question the likelihood of that happening as it is very likely that
most of the cpu time will be spent outside of oslo messaging code.
Furthermore, Python does not need multiple threads to go faster. As a matter of
fact, for in-memory operations, it could end up being slower because of the
inherent design of the interpreter (and there are many independent measurements
that have shown it).
And this is unmodified/highly unoptimized oslo messaging code.
If you remove the oslo messaging layer, you get 25000 to 45000 msg/sec with
kombu/rabbitMQ (which shows how inefficient is oslo messaging layer itself)
So I'm pretty sure this is o-k for small clouds, but would be
a disaster for a large, busy cloud.
It all depends on how many sched/sec for the "large busy cloud"...
I think there are two interesting things to discern. Of course, the
exact rate would be great to have as a target, but operational security
and just plain secrecy of business models will probably prevent us from
getting at many of these requirements.
I don't think that is the case. We have no visibility because nobody has really
thought about these numbers. Ops should be ok to provide some rough requirement
numbers if asked (everybody is in the same boat).
The second is the complexity model of scaling. We can just think about
the actual cost benefit of running 1, 3, and more schedulers and come up
with some rough numbers for a lower bounds for scheduler performance
that would make sense.
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.
How many compute nodes are we talking about max? How many scheduling per second
is the requirement? And where are we today with the latest nova scheduler?
My point is that without these numbers we could end up under-shooting,
over-shooting or over-engineering along with the cost of maintaining that extra
complexity over the lifetime of openstack.
I'll just make up some numbers for the sake of this discussion:
nova scheduler latest can do only 100 sched/sec for 1 instance (I guess the
10ms average you bring out may not be that unrealistic)
the requirement is a sustained 500 sched/sec worst case with 10K nodes (that is
5% of 10K and today we can barely launch 100VM/sec sustained)
Are we going to achieve 5x with just 3 instances which is what most people
deploy? Not likely.
Will using more elaborate distributed infra/DLM like consul/zk/etcd going to
get us to that 500 mark with 3 instances? Maybe but it will be at the expense
of added complexity of the overall solution.
Can we instead optimize nova scheduler with single instance to do 500/sec?
Maybe but if we succeed we'll get a lot more simple solution.
Of course we can. And simple solutions are great. So if you can get one
node to do all the work and the cloud doesn't fall over dead when it dies
(because you have more waiting on standby), that would be fantastic.
I'm dubious that this will be a solution that works for large deployers.
But I may be wrong!
Control plane apps that had to do more complex work than scheduling instances
have been working for a long time at scale using simple active/standby designs.
In this case it is easier because we can even afford some scheduling disruption
when your active goes down (as long as the standby can pick up most of the
pending requests). Heck when an entire openstack controller goes down, I bet
failing a few request will be the least of your concerns (because a lot more
other things will go wrong).
Not saying that high concurrency and distributed schedulers is not the solution
and maybe we really need a distributed solution, but it'd be good to have some
numbers to frame the discussion.
Indeed, however, I don't think we can expect those numbers to materialize
in public, ever. We have to make some informed guesses and see how the
product managers respond when we tell them what we think should happen. ;)
Isn't that a concern? Shouldn't the TC provide at least some numbers for the
scale range so we do not under/over engineer? Any number would be better than
no number at all.
It is an open secret that Nova/Neutron does not scale well below the 1000-node
mark (400/500 seems to be a threshold to do anything serious in production).
If we look at all the openstack/Neutron deployments today, nobody has an exact
count, but very likely the vast majority of deployments are smaller than 100
nodes. Few are over 100 nodes, even less over 500 (well we know some
companies/orgs have deployed thousands but none with Neutron).
For those 99% of deployments, we only need to service less than 100 nodes. In that space
the inability to schedule over 100 instances per second is probably not that important.
If the problem we are trying to solve here is "scheduling is too slow", then
the first question to ask is how much faster do we need to go, then brainstorm on what
would be the best way to achieve.
I have no problem believing we can schedule a lot more instances/sec by using
scale out design with DLMs but there is no free lunch. Is it fair to impose on
those 99% deployments the complexity of a machinery that is designed to handle
1000 schedules/sec? Ops are very concerned about adding even more complexity to
an already complex platform and I think we should be considerate to that,
meaning worry about the cost impact in HW, config, deployment, troubleshooting.
