[Beowulf] Lustre on google cloud

Chris Dagdigian dag at sonsorol.org
Fri Jul 26 04:26:12 PDT 2019

Coming back late to this thread as yesterday was a travel/transit day 
... some additional thoughts

1) I also avoid the word "cloud bursting" these days because it's been 
tarred by marketing smog and does not mean much. The blunt truth is that 
from a technical perspective having a hybrid premise/cloud HPC is very 
simple. The hard part is data -- either moving volumes back and forth or 
trying to maintain a consistent shared file system at WAN-scale 
networking distances.

The only successful life science hybrid HPC environments I've really 
seen repeatedly are the ones that are chemistry or modeling focused 
because generally the chemistry folks have very small volumes of data to 
move but very large CPU requirements and occasional GPU needs. Since the 
data movement requirements are small for chemistry it's pretty easy to 
make them happy on-prem, on the cloud or on a hybrid design

Not to say full on cloud bursting HPC systems don't exist at all of 
course but they are rare. I was talking with a pharma yesterday that 
uses HTcondor to span on-premise HPC with on demand AWS nodes. I just 
don't see that as often as I see distinct HPCs.

My observed experience in this realm is that for life science we don't 
do a lot of WAN-spanning grids because we get killed by the 
gravitational pull of our data. We build HPC where the data resides and 
we keep them relatively simple in scope and we attempt to limit WAN 
scale data movement. For most this means that having onsite HPC and 
cloud HPC and we simply direct the workload to whichever HPC resource is 
closest to the data.

So for Jörg -- based on what you have said I'd take a look at your 
userbase, your application mix and how your filesystem is organized. You 
may be able to set things up so that you can "burst" to the cloud for 
just a special subset of your apps, user groups or data sets. That could 
be your chemists or maybe you have a group of people who regularly 
compute heavily against a data set or set of references that rarely 
change -- in that case you may be able to replicate that part of your 
GPFS over to a cloud and send just that workload remotely, thus freeing 
up capacity on your local HPC for other work.

2) Terabyte scale data movement into or out of the cloud is not scary in 
2019. You can move data into and out of the cloud at basically the line 
rate of your internet connection as long as you take a little care in 
selecting and tuning your firewalls and inline security devices. 
Pushing  1TB/day etc.  into the cloud these days is no big deal and that 
level of volume is now normal for a ton of different markets and 
industries.   It's basically a cost and budget exercises these days and 
not a particularly hard IT or technology problem.

There are two killer problems with cloud storage even though it gets 
cheaper all the time

2a) Cloud egress fees.  You get charged real money for data traffic 
leaving your cloud. In many environments these fees are so tiny as to be 
unnoticeable noise in the monthly bill. But if you are regularly moving 
terabyte or petabyte scale data into and out of a cloud provider then 
you will notice the egress fees on your bill and they will be large 
enough that you have to plan for them and optimize for cost

2b) The monthly recurring cost for cloud storage can be hard to bear at 
petascale unless you have solidly communicated all of the benefits / 
capabilities and can compare them honestly to a full transparent list of 
real world costs to do the same thing onsite.  The monthly s3 storage 
bill once you have a few petabytes in AWS is high enough that you start 
to catch yourself doing math every once in a while along the lines of "I 
could build a Lustre filesystem w/ 2x capacity for just 2-months worth 
of our cloud storage opex budget!"

> INKozin via Beowulf <mailto:beowulf at beowulf.org>
> July 26, 2019 at 4:23 AM
> I'm very much in favour of personal or team clusters as Chris has also 
> mentioned. Then the contract between the user and the cloud is 
> explicit. The data can be uploaded/ pre staged to S3 in advance (at no 
> cost other than time) or copied directly as part of the cluster 
> creation process. It makes no sense to replicate in the cloud your 
> in-house infrastructure. However having a solid storage base in-house 
> is good. What you should look into is the cost of transfer back if you 
> really have to do it. The cost could be prohibitively high, eg if Bam 
> files need to be returned. I'm sure Tim has an opinion.
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> Joe Landman <mailto:joe.landman at gmail.com>
> July 26, 2019 at 12:00 AM
> The issue is bursting with large data sets.  You might be able to 
> pre-stage some portion of the data set in a public cloud, and then 
> burst jobs from there.  Data motion between sites is going to be the 
> hard problem in the mix.  Not technically hard, but hard from a 
> cost/time perspective.
> Jörg Saßmannshausen <mailto:sassy-work at sassy.formativ.net>
> July 25, 2019 at 8:26 PM
> Dear all, dear Chris,
> thanks for the detailed explanation. We are currently looking into cloud-
> bursting so your email was very timely for me as I am suppose to look 
> into it.
> One of the issues I can see with our workload is simply getting data 
> into the
> cloud and back out again. We are not talking about a few Gigs here, we 
> are
> talking up to say 1 or more TB. For reference: we got 9 PB of storage 
> (GPFS)
> of which we are currently using 7 PB and there are around 1000+ users
> connected to the system. So cloud bursting would only be possible in some
> cases.
> Do you happen to have a feeling of how to handle the issue with the 
> file sizes
> sensibly?
> Sorry for hijacking the thread here a bit.
> All the best from a hot London
> Jörg
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> Chris Dagdigian <mailto:dag at sonsorol.org>
> July 22, 2019 at 2:14 PM
> A lot of production HPC runs on cloud systems.
> AWS is big for this via their AWS Parallelcluster stack which does 
> include lustre support via vfXT for lustre service although they are 
> careful to caveat it as staging/scratch space not suitable for 
> persistant storage.  AWS has some cool node types now with 25gig, 
> 50gig and 100-gigabit network support.
> Microsoft Azure is doing amazing things now that they have the 
> cyclecomputing folks on board, integrated and able to call shots 
> within the product space. They actually offer bare metal HPC and 
> infiniband SKUs now and have some interesting parallel filesystem 
> offerings as well.
> Can't comment on google as I've not touched or used it professionally 
> but AWS and Azure for sure are real players now to consider if you 
> have an HPC requirement.
> That said, however, a sober cost accounting still shows on-prem or 
> "owned' HPC is best from a financial perspective if your workload is 
> 24x7x365 constant.  The cloud based HPC is best for capability,  
> bursty workloads, temporary workloads, auto-scaling, computing against 
> cloud-resident data sets or the neat new model where instead of 
> on-prem multi-user shared HPC you go out and decide to deliver 
> individual bespoke HPC clusters to each user or team on the cloud.
> The big paradigm shift for cloud HPC is that it does not make a lot of 
> sense to make a monolithic stack shared by multiple competing users 
> and groups. The automated provisioning and elasticity of the cloud 
> make it more sensible to build many clusters so that you can tune each 
> cluster specifically for the cluster or workload and then blow it up 
> when the work is done.
> My $.02 of course!
> Chris

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