--- In NADUG@yahoogroups.com, "Zotter, E. David" <david@...> wrote:
>
> How many engines are you running on your typical online/web
cluster for mrinterview?
>
> One of our clusters has 9 engines per app server for a total of 36
and the other has 4 engines per app server for a total of 8.
>
> We're trying to figure out if it is better to have more engines
doing less work each or less engines doing more work each.
>
>
> I'm curious to hear what others are doing.
>
> Thanks,
>
> -David
>

Good question David. We've deployed a couple different
configurations at this point, although if I'm honest, I can't say we
have a lot of solid reasoning behind the choices we've made.

We currently haven't extended beyond six engines per app server yet
(total number of engines per cluster varies typically from 18 to
24). One factor that has acted as an artificial threshold of sorts
is the need to use an Enterprise-level OS for servers with more than
4GB. But if I thought I could deploy 16 engines on a single server
with no downside, the extra costs for the Enterprise OS would be a
no-brainer to me.

We are following the general guideline communicated to us long ago
by SPSS to allocate 1GB of physical RAM per engine. With the use of
virtualization, we've been able to take advantage of that more
readily, but still face the OS memory limitation if we deployed with
the Standard OS.

The key driver for adding engines at this point seems to be memory,
memory, and memory. Some engines will struggle with a relatively
low number of live interview sessions because of a huge per-session
memory footprint attributed to a project or two. This potential for
variance at the project-level is very frustrating when trying to
project the capacity of a given cluster.

Do you segment out your app servers from the other tiers? How many
app servers are you using in your cluster configurations?