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Comment by vergessenmir | Hacker News Reader

Lift and shift is a cloud migration strategy which involves moving your applications to the cloud with little to no modification. For example, you have an application running on a server in your data-centre, you then deploy a VM in the cloud with a similar spec and install the application.

It's usually done to avoid the engineering cost of making the services more cloud native. What tends to happen a lot is that after a considerable portion of the migration is completed, the cost of the lift-and-shift effort start to overtake the savings, and the projected costs, dwarf the future savings.

I suspect this is what happened with Linkedin.

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which savings ? It’s never been obvious to me that cloud was cheaper if you’re a large company

It’s easier to scale cloud infrastructure.

Even if you never need to scale it's cheaper to not have to physically maintain your own data center. If all the broken server, building power, building internet, access control, real estate costs... are all handled in the cloud there's savings there as well.

But those costs don't go away -- the cloud provider is going to charge you for them, along with a premium for profit?

I'm used to organizations moving out of the cloud when they realize that it's more expensive if you don't have very peaky load demands.

Very few people who don't use cloud use their own data centers. The dominant alternative to cloud is colo or managed servers.

And it's difficult to make that as expensive as a cloud deployment.

But that's somewhat negated if you lift and shift, because your application is not designed to leverage that capability in that way.

Compute is at a premium, but you can shift opex/capex around which might be more suitable. It can also be cheaper in headcount since you need fewer operators and less expertise in datacenter operations.

> you need fewer operators and less expertise in datacenter operations

Because you are paying someone else for them.

This is considered rational because those operators are presumably more productive in a pool of people using similar skills to support many customers rather than just one. It is similar to hiring a cleaning service rather than employing individual cleaners in a department of cleaning because cleaning things is not a core competency of business.

It might be less irrational if some amount of compute is part of the core competency of the business. Since "software is eating the world," compute is a core competency of all businesses except for the ones that don't realize it yet.

I think there is a difference in competency between using Cloud Compute and running your own Datacenter. Perhaps some companies have the overlap, but I suspect this is an additional skillset they need to cultivate to achieve the savings.

> It can also be cheaper in headcount since you need fewer operators and less expertise in datacenter operations.

I've not really seen this work out well. I think it might be true for simple set-ups, letting a tiny developer team also handle infra and support without going nuts doing it, if they set it up that way from the beginning, but more-complex setups always seem to have so damn many sharp edges and moving pieces that support ends up looking similar to what a far more DIY approach (short of building one's own datacenter outright) would, in terms of time lost to it.

... and so does downtime, for that matter.

When I've done devops consulting, clients with cloud deployments invariably spent more money on devops because of the often significantly more complex environments.

It's at least more predictable. You don't pay for staff with datacenter skills (sort of in short supply) and you don't need to make large investments early on to build the datacenter and you don't have a huge headache if you need to scale up or down operations.

It really depends on workloads. Imagine you need massive spikes of compute for, say, flash sales, or people watching the superbowl in your streaming service. Buying all that hardware for just the spikes might not make sense vs just scaling up vms in a cloud provider and scale them down.

In the real world, for baseline load, the big advantage for many large companies isn't price, but the massive lack of alacrity of many inhouse ops teams. If it takes me 3+ months to provision compute for the simplest, lowest demand services (as is the custom in many large companies full of red tape and arguments about who bears costs), letting teams just spin up anything they want and get billed directly is often a winner, even if it's more expensive. Having entire teams waste months before they can put something in prod is a very different kind of expense in itself.

The simplest example is if you have on-prem hardware, you need to have capacity for your peak load. In a lift and shift, you would replace your fleet of 96 core xeons with a fleet of 96 core xeons in AWS.

The cloud native approach would be to modify your app so that it can be scaled up and down so you keep a few machines always running, and scale up and down with your traffic so you only run at capacity when you need it.

This doesn’t demonstrate anything about the savings.

Anecdotally, when my previous company was looking at costs, cloud unequivocally came out significantly more expensive, and that wasn’t even a large company (only 2,000 or so employees).

I will grant that we did not have globalization problems to solve (but I’d also wager that lots of businesses prematurely “what if” this scenario anyway).

> This doesn’t demonstrate anything about the savings.

If you neeed 4 CPUs for your peak load for 4 hours per day, and only 1 of them for the other 20 hours a day, you can save by scaling down to 1 cpu for 85% of the day.

This assumes a lot about the cost of those CPUs and related resources in the respective environments. Cost per equivalently performing unit on a managed server vs. cloud instances is often vastly different.

It's also extremely uncommon to have loads that spiky.

And when you do, hybrid is often a solution (use a provider that can provide colo or managed servers for your base load and cloud instances for your peaks, or scale across providers).

It's pretty hard to capture the nuance of any possible solution in 2 paragraphs without someone coming along and picking it apart. The guy I replied to didn't know even the most basic information.

Even at that, you said yourself that you can use "cloud" to scale into your spikes.

Yes, but ironically being prepared to handle spikes with cloud tends to make it even less cost effective to do so, because it means you can plan for far higher utilisation of your on prem/colo/managed servers with little risk.

It takes very unusual load patterns for cloud to win on cost. It does occasionally happen, but far less often than developers tend to think.

There many reasons to choose cloud services, but cost is almost never one of them.

> It does occasionally happen, but far less often than developers tend to think.

Guy asked what is a cloud workload, I responded. Nitpicking every tiny detail doesn't help.

> There many reasons to choose cloud services, but cost is almost never one of them.

It's cheaper to pay me to manage IAM roles for lambdas and ECS instances for 5% of my time than it is to pay someone full-time to manage some sort of VMware or other system. It's easier and cheaper to find someone with experience with AWS who can provide value to the team and product than it is to find someone who can manage and maintain a cobbled together set of scripts to update apps. There are click and go options for deploying major self hosted services like grafana, k8s with secure details that I can use without spending any time (and time == $$$) learning about the developers preferred deployment scheme.

This isn't nitpicking, it's why the cloud option is very rarely cheapest. I've costed this out for many organizations over the years and tested the assumptions.

> It's cheaper to pay me to manage IAM roles for lambdas and ECS instances for 5% of my time than it is to pay someone full-time to manage some sort of VMware or other system.

True, but it's a false equivalence, and one I often see used from people unaware of the ease of contracting this out on a fractional basis.

I used to make a living cleaning up after people who thought cloud was easier, who ended up often spending a fortune untangling years of accumulated cruft that just never happened for my non-cloud customers.

The hiccup with this arithmetic is that the big cloud providers charge 7x to 10x the price you’d pay for an on-premises VM.

Sure, sure, you’re about to say something about discounts? Granted, that’s available, but only for commitments starting at one year or longer!

Okay, fine, I actually agree that there are savings available by reducing head count. The entire network and storage teams can be made redundant, for starters. Even considering that DevOps and cloud infra engineers need to be hired at great expense, this can be a net win…

…but isn’t in my experience. Managers are unwilling or unable to make many people redundant at once, so they stick around and find things to do…

… things like reproducing the mess that kept them employed, but in the cloud.

I’m watching this unfold at about a dozen of my large enterprise customers right now.

Got to get back to work and send the fifty seventh email about spinning up a single VM. Got to run that past every team! It’s no rush, it’s only been about fourteen months now…

Although, it must be unusual, right? This is not one company porting their service to the cloud, this is Microsoft porting their LinkedIn service from whatever servers came along with LinkedIn, to their own servers, on which they also run a cloud business.

Which… isn’t to say anything about which way we should expect that to swing things. But it seems quite unusual, as most companies have not been bought by a cloud provider. Yet…

> this is Microsoft porting their LinkedIn service from whatever servers came along with LinkedIn, to their own servers

Nope, LinkedIn executes completely independently.

That is not really true, if it was then Linkedin would have evaluated all the clouds in fair process and gone with AWS as the best fit in 2019 if they wanted to move in the first place.

The pressure to move to a cloud and to Azure specifically both comes entirely from MS. Linkedin was perfectly happy then and now running its own setup, this 4 years of trying to move is because of MS ownership.

If your architecture is chatty enough, you will be sharding things so that most traffic stays in one rack, room, or data center.

If you treat us-west-1 as a single data center, you may find you are spending a lot on traffic between AZs.

A lift and shift might treat us-west-1 like a single data center. A more sophisticated strategy might treat it as three.

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