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Goodbye AWS: Rolling your own servers with Kubernetes, Part 1

Goodbye AWS: Rolling your own servers with Kubernetes, Part 1

May 5, 2019 by Ev Kontsevoy

Why leave AWS?

In this three-part blog series, we’ll try to address some of the fears and uncertainties faced by organizations who had successfully started their projects on public clouds, like AWS, but for one reason or another found themselves needing to replicate their cloud environment from scratch, starting with an empty rack in their own enterprise server room or a colocation facility.

If you are reading this, perhaps you already know why it makes sense in your case. If you are just curious, as makers of open source tools for Kubernetes application packaging and server access management, here is what we have heard from users of our software:

There are myriad other reasons. The colocation market is still growing, after all, so let’s get the question “why” out of the way and focus on “how”.

Rolling your own servers can be a daunting proposition, especially for younger technologists who are used to bootstrapping formidable server fleets with an API key, not with their bare hands covered in a mixture of blood, sweat and a server rail grease. However, it can be done with sizable cost, performance and compliance benefits and Kubernetes (aka, K8s) can be your secret weapon!

"... Sometimes I feel nobody gives me no warning Find my head is always up in the clouds in a dream world It's not easy, living on my own..." - Freddie Mercury

Why Kubernetes?

We can all agree that our industry is prone to hype and sometimes we feel the pressure of adopting a new technology simply because our peers and competitors do. Before diving into challenges of adopting Kubernetes, lets remind ourselves of why someone should (or should not) bother.

The primary benefit of K8s is to increase infrastructure utilization through the efficient sharing of computing resources across multiple processes. As your organization adopts more workloads of varying performance envelopes, the art of bin packing hundreds of micro-services across available computing resources becomes more and more critical. Kubernetes is the master of dynamically allocating computing resources to fill the demand. This allows users to define infrastructure requirements for their applications using code, and shortens the time to production significantly relative to trying to manually “bin pack” your micro-services into a static hardware cabinet.

In other words, in addition to dynamic resource scheduling, Kubernetes allows users to realize many of the cloud benefits while running on bare metal servers.

Should you roll your own servers?

If you are not certain, the answer is most likely no. The staggering growth of AWS happened for a reason. Software-defined and globally distributed infrastructure which allows users to pay only for the resources they are using on a per-second basis is incredible. (If you are a typical SaaS provider that is entertaining the idea of managing your own servers, Gitlabs exploration into going off the cloud is a good overview of how they came to the determination it was not the right choice for them.)

This post is for those who either don’t have the luxury of using a cloud provider or who have reached the conclusion that the benefits of colo outweigh the costs of using a cloud provider.

The number one piece of advice is to hire someone who has done this before. If you do that you probably (hopefully) don’t need this blog post. However, this post may help you hire that person or may help you evaluate whats involved.

As mentioned above, a reason for expanding beyond public clouds includes wanting a better cost/performance ratio that can be realized by having complete control over the infrastructure tuning.

One example of such tuning can be the need to run a large amount of mostly idle instances of a cloud application, perhaps as POCs for customers or sales demos. A physical server with 20 real cores can run a hundred VMs with their own virtual vCPUs (and you can over-provision RAM too!)

This is a blog post, not a book, so lets keep it focused and make a few assumptions about our mini-datacenter to limit the scope:

Colocation and Hardware

If you are rolling your own hardware, you need a place to put your machines. You generally don’t want them next to you unless you have really good noise-cancelling headphones and/or you need a strong heater. Companies who rent “pieces of data centers” are called colocation facilities and the “pieces” they rent are called server cabinets (or racks). Cabinets usually have individual locks, but if additional physical security is required, you may request a “cage” i.e. a separate room with its own lock which will host all of your server cabinets.

There are several form factors for server cabinets to be aware of. It is easier to get started with a standard 19-inch cabinet and your local colo facility will likely have that to offer. If you are going to become a datacenter nerd, you will eventually discover the OpenCompute project (OCP), which have different dimensions. That’s a sexy subject on its own and it probably deserves a separate blog post.

A rack is divided into logical units of vertical space called a “U”. Most data center racks have a height of 42Us. This means they can hold up to 42 of 1U servers. However, you’ll probably need some Us for the network gear as well.

There are many factors to consider when picking server hardware. Due to the recent price drops for flash memory and the resurgence of AMD as a viable competitor to Intel, we now have access to incredible performance concentrated in a small amount of space. Heres an example of a system you can have:

Even for our limited deployment, we can easily provision over a thousand physical CPU cores and 40+ terabytes of RAM in a single cabinet! Efficiently managing this amount of processing power is impossible by hand. That is where private cloud software, or Kubernetes in our case, comes in handy.

The hardware above is quite vanilla. But as we mentioned above, some companies use colocation to take advantage of specialized hardware such as FPGAs, GPUs or even consumer-grade CPUs because they offer much higher single thread performance, often at the expense of not having ECC memory support or limited I/O throughput.

Redundancy

An important topic probably worth touching on is designing for failure. Which things need redundancy? Is that achieved in hardware (HW) or in software (SW)? The trend is generally to move away from achieving HA via HW and instead using SW. K8s will move containers around if a server fails, so servers dont need HW redundancy (e.g. HA power or HA network interface controllers).

Although, you need to leave sufficient cluster capacity. Even if each server doesnt need HA power, you may want to straddle servers in the cluster and put 1⁄2 on circuit A and 1⁄2 on circuit B, provided those have upstream power failure isolation from the colo provider (this is somewhat of a poor man’s availability zone).

To limit the scope of this article, let’s assume that we do no need separate availability zones in your setup, but it’s worth mentioning that the level of desired redundancy is an huge factor in the cost!

Now, lets talk about money.

When you call a colocation company for a price quote, the most common first question theyll ask will be: “how much power do you need?” The colo industry is basically reselling electricity at a premium. Your answer needs to be in amps (A), where:

A = power (W) / voltage (V)

i.e. a single server consuming 300w will need 2.5 amps at 120 volts. The power is usually sold on a per-cabinet basis, i.e. if you bought a 15amp cabinet, it means you can only fit six servers into one. Power is all they care about and it is not uncommon to receive a fixed-price quote for pre-provisioned electricity regardless of how much bandwidth youd consume. A “starter pack” 15amp cabinet with a gigabit connection can be rented for as little as $400 per month.

The prices will vary, but the number one question you probably have is: “is it cheaper than AWS?” The only answer I am comfortable saying is it depends. If you already have the skills to manage the infrastructure and your servers will be well-utilized most of the time, then the DIY route will be cheaper. But if your workloads are highly variable, you may find yourself massively over-provisioning and paying for resources that are idle most of the time. Another major expense of DIY is having to hire a team of SREs to keep your databases and other infrastructure software running smoothly.

Before concluding that colocation is vastly cheaper, please bear in mind that AWS also offers unique billing models not available in DIY deployments, namely spot instances and reserved instances. Spot instances allow you to run workloads that can tolerate interruption (e.g. non-time sensitive batch processing jobs) at a significant discount (50-80%). AWS lets you bid a lower price for EC2 instances and when AWS demand necessitates more capacity, they reclaim your spot instances with a few minutes notice. Reserved instances are meant to mimic purchasing your own physical servers which typically have a 3 year life / amortization. You make a commitment to run a particular EC2 instance for 1 or 3 years in exchange for 30-70% discount. Convertible reserved instances are even more flexible and let you exchange your commitments over time as you EC2 instance needs change. Just remember that managing reserved instances can be very difficult and you can actually end up paying more than on-demand rates if you aren’t careful.

Before leaving AWS strictly because of cost-related concerns, we recommend checking out vendors who do automated reserved instance management.

Next…

So far, we have covered:

In the next chapter, we’ll cover building a bare metal network to run Kubernetes, among other things.

Stay tuned!

Thanks to Aaron Sullivan and Erik Carlin for reading the draft of this post and providing valuable suggestions.

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