I wonder why someone would equate "instance launch time" with "reliability"... I won't go as far as calling it "clickbait" but wouldn't some other noun ("startup performance is wildly different") have made more sense?
Well, if your system elastically uses GPU compute and needs to be able to spin up, run compute on a GPU, and spin down in a predictable amount of time to provide reasonable UX, launch time would definitely be a factor in terms of customer-perceived reliability.
All the clouds are pretty upfront about availability being non-guaranteed if you don't reserve it. I wouldn't call it a reliability issue if your non-guaranteed capacity takes some tens of seconds to provision. I mean, it might be your reliability issue, because you chose not to reserve capacity, but it's not really unreliability of the cloud — they're providing exactly what they advertise.
"Guaranteed" has different tiers of meaning - both theoretical and practical.
In many cases, "guaranteed" just means "we'll give you a refund if we fuck up". SLAs are very much like this.
IN PRACTICE, unless you're launching tens of thousands of instances of an obscure image type, reasonable customers would be able to get capacity, and promptly from the cloud.
That's the entire cloud value proposition.
So no, you can't just hand-waive past these GCP results and say "Well, they never said these were guaranteed".
Ignoring the fact that the results are probably partially flawed due to methodology (see top-level comment from someone who works on GCE) and are not reproducible due to missing information, pointing out the lack of a guarantee is not hand-waving. The OP uses the word "reliability" to catch attention, which certainly worked, but this has nothing to do with reliability.
This isn't actually true, even for tiny customers. In a personal project, I used a single host of a single instance type several times per day and had to code up a fallback.
Try spinning up 32+ core instances with local ssds attached or anything not n1 family and you will find that in may regions you can only have like single digits of them
I'd still consider it as "performance issue", not "reliability issue". There is no service unavailability here. It just takes your system a minute longer until the target GPU capacity is available. Until then it runs on fewer GPU resources, which makes it slower. Hence performance.
The errors might be considered a reliability issue, but then again, errors are a very common thing in large distributed systems, and any orchestrator/autoscaler would just re-try the instance creation and succeed. Again, a performance impact (since it takes longer until your target capacity is reached) but reliability? not really
I’d like to see a breakdown of the cost differences. If the costs are nearly equal, why would I not choose the one that has a faster startup time and fewer errors?
With GCP you can right-size the CPU and memory of the VM the GPU is attached to, unlike the fixed GPU AWS instances, so there is the potential for cost savings there.
It is not reliably running the machine but reliably getting the machine.
Like the article said, The promise of the cloud is that you can easily get machines when you need them the cloud that sometimes does not get you that machine(or does not get you that machine in time) is a less reliable cloud than the one that does.
It’s still performance. If this was “AWE failed to deliver the new machines and GCP delivered”, sure, reliability. But this isn’t that.
The race car that finishes first is not “more reliable” than the one in 10th. They are equally as reliable, having both finished the race. The first place car is simply faster at the task.
You cannot infer that based on the results of the race...that's literally the entire point I am making. The 1st place car might blow up in the next race, the 10th place car might finish 10th place for the next 100 races.
If the article were measuring HTTP response times and found that AWS's average response time was 50ms and GCP's was 200ms, and both returned 200s for every single request in the test, would you say AWS is more reliable than GCP based on that? Of course not, it's asinine.
If you want that promise you can reserve capacity in various ways. Google has reservations. Folks use this for DR, your org can get a pool of shared ones going if you are going to have various teams leaning on GPU etc.
The promise of the cloud is that you can flexibly spin up machines if available, and easily spin down, no long term contracts or CapEx etc. They are all pretty clear that there are capacity limits under the hood (and your account likely has various limits on it as a result).
unfortunately cloud computing and marketing have conflated reliability, availability and fault tolerance so it's hard to give you a definition everyone would agree to, but in general I'd say reliability is referring to your ability to use the system without errors or significant decreases in throughput, such that it's not usable for the stated purpose.
in other words, reliability is that it does what you expect it to. GCP does not have any particular guarantees around being able to spin up VMs fast, so its inability to do so wouldn't make it unreliable. it would be like me saying that you're unreliable for not doing something when you never said you were going to.
if this were comparing Lambda vs Cloud Functions, who both have stated SLAs around cold start times, and there were significant discrepancies, sure.
true, the grammar and semantics work out, but since reliability needs a target usually it's a serious design flaw to rely on something that never demonstrably worked like your reliability target assumes.
so that's why in engineering it's not really used as such. (as far as I understand at least.)
Why would you scale to zero in high perf compute? Wouldn't it be wise to have a buffer of instances ready to pick up workloads instantly? I get that it shouldnt be necessary with a reliable and performant backend, and that the cost of having some instances waiting for job can be substantial depending on how you do it, but I wonder if the cost difference between AWS and GCP would make up for that and you can get an equivalent amount of performance for an equivalent price? I'm not sure. I'd like to know though.
> Why would you scale to zero in high perf compute?
Midnight - 6am is six hours. The on demand price for a G5 is $1/hr. That's over $2K/yr, or "an extra week of skiing paid for by your B2B side project that almost never has customers from ~9pm west coat to ~6am east coast". And I'm not even counting weekends.
But that's sort of a silly edge case (albeit probably a real one for lots of folks commenting here). The real savings are in predictable startup times for bursty work loads. Fast and low variance startup times unlock a huge amount of savings. Without both speed and predictability, you have to plan to fail and over-allocate. Which can get really expensive fast.
Another way to think about this is that zero isn't special. It's just a special case of the more general scenario where customer demand exceeds current allocation. The larger your customer base, and the burstier your demand, the more instances you need sitting on ice to meet customers' UX requirements. This is particularly true when you're growing fast and most of your customers are new; you really want a good customer experience every single time.
Scaling to zero means zero cost when there is zero work. If you have a buffer pool, how long do you keep it populated when you have no work?
Maintaining a buffer pool is hard. You need to maintain state, have a prediction function, track usage through time, etc. just spinning up new nodes for new work is substantially easier.
And the author said he could spin up new nodes in 15 seconds, that’s pretty quick.
For this, I'd prefer a title that lets me draw my own conclusions. 84 errors out of 3000 doesn't sound awful to me...? But what do I know – maybe just give me the data:
"1 in 3000 GPUs fail to spawn on AWS. GCP: 84"
"Time to provision GPU with AWS: 11.4s. GCP: 42.6s"
"GCP >4x avg. time to provision GPU than AWS"
"Provisioning on GCP both slower and more error-prone than AWS"
Another comment on this thread pointed out they had a potential collision in their instance name generation which may have caused this. That would mean this was user error, not a reliability issue. AWS doesn’t require instance names to be unique.
Maybe 1 reported. Not saying aws reliability is bad, but the number of various glitches that crop up in various aws services and not reflected on their status page is quite high.
GCP error messages will indicate if resources were not available, if you reached your quota, or if it was some other error. Tests like OP can differentiate these situations
Still, 409 seems inappropriate, as it is meant to signal a version conflict, i.e. someone else changed something, and user tried to uplod a stale version.
”10.4.10 409 Conflict
The request could not be completed due to a conflict with the current state of the resource. This code is only allowed in situations where it is expected that the user might be able to resolve the conflict and resubmit the request. The response body SHOULD include enough information for the user to recognize the source of the conflict. Ideally, the response entity would include enough information for the user or user agent to fix the problem; however, that might not be possible and is not required.
Conflicts are most likely to occur in response to a PUT request. For example, if versioning were being used and the entity being PUT included changes to a resource which conflict with those made by an earlier (third-party) request, the server might use the 409 response to indicate that it can't complete the request. In this case, the response entity would likely contain a list of the differences between the two versions in a format defined by the response Content-Type.”
Then again, perhaps it is the service itself making that state change.
Using HTTP error codes for non-REST things is cringe.
503 would mean the IaaS API calls themselves are unavailable. Very different from the API working perfectly fine but the instances not being available.
What? REST is just some API philosophy, its doesn't even have to be on top of HTTP.
Why would you think HTTP status codes are made for REST? They are made for HTTP to describe the response of the resource you are requesting, and the AWS API uses HTTP so it makes sense to use HTTP status codes.
Cloud reliability is not the same as a reliability of already spawned VM.
Here it's the possibility to launch new VMs to satisfy dynamic projects' needs. Cloud provider should allow you to scale-up in a predictable way. When it doesn't - it can be called unreliable.
Also, "unreliable" is basically a synonym for "Google" these days.
If I depend on some performance metric, startup, speed, etc, my dependance on it equates to reliability. Not just on/off but the spectrum that it produces.
If a CPU doesn't operate at its 2GHz setting 60% of the time, I would say that's not reliable. When my bus shows up on time only 40% of the time - I can't rely on that bus to get me where I need to go consistently.
If the GPU took 1 hour to boot, but still booted, is it reliable? What about 1 year? At some point it tips over an "personal" metric of reliability.
The comparison to AWS which consistently out-performs GCP, while not explicitly, implicitly turns that into a reliability metric by setting the AWS boot time as "the standard".
I mean if you're talking about worst case systems you assume everything is gone except your infra code and backups. In that case your instance launch time would ultimately define what your downtime looks like assuming all else is equal. It does seem a little weird to define it that way but in a strict sense maybe not.
Well, I mean it is measuring how reliably you can get a GPU instance. But it certainly isn't the overall reliability. And depending on your workflow, it might not even be a very interesting measure. I would be more interested in seeing a comparison of how long regular non-GPU instances can run without having to be rebooted, and maybe how long it takes to allocate a regular VM.
Reliability is a fair term, with an asterix. It is a specific flavor of reliability: deployment or scaling or net-new or allocation or whatever you want to call it.
They are talking about the reliability of AWS vs GCP. As a user of both, I'd categorize predictable startup times under reliability because if it took more than a minute or so, we'd consider it broken. I suspect many others would have even tighter constraints.
