This post reviews the criteria for selecting a cloud computing technology and shows a couple of examples. Whenever I try to explain the choices involved, I usually manage to forget one or two. So this is an attempt to capture the entirety so that I have a better chance of remembering. Or I can give this link out. :)

The bottom line is that outsourcing computing and storage to the cloud has benefits. Storage is very expensive, but those costs allow the servers to be used efficiently. Startups along with application development and testing are likely to derive the greatest benefit from moving to an external cloud. Cloud computing is also beneficial if the application capacity planning has enough overhead included to push the private data center costs over what the monthly cloud costs are.

Using a Cloud

There are at least two reasons a company would want to use cloud computing. The first case – what is usually assumed – is that the cloud will be used to provide a production service. The other case, which is also an efficient use of cloud computing, is to use the cloud resources for application development.

When using cloud computing resources to provide production services, an additional benefit is that applications can elastically add more resources as load increases, and release these resources once load eases. In cases where the load changes slowly or in a known time varying pattern this can be a considerable benefit. It allows the production services to be provided without providing peak capacities in a dedicated server facility.

When developing applications using cloud resources, they tend to be large data intensive applications that scale out (horizontally) using cloud resources only when needed. Developing these applications in a cloud allows the development, configuration, and deployment to be performed and tested prior to committing resources (cash!) for the infrastructure required to field these applications.

A Motivating Example

Instead of deriving a large complex calculation to determine costs, I want to make a couple of (ok, a few. Would you believe just slightly more than a few?) simplifying assumptions so we can get a feel for what the magnitudes are and what the trade offs might be.

So, with that in mind, lets look at c9.inc, a fictional new startup that intends to field a service that will require large processing and storage capacities. Lets start with the processing capacity. Lets make the assumption that the application architects have accurately predicted the required resources and that a data center with 1,024 cores will provide the anticipated needs for the near term. Since we know this is a data intensive application assume that the storage needs will be for 1,000 TB of data.

Example Costs

In the following, I will make some assumptions and derive a cost for both a cloud based and a private facility based solution that can provide the capacity required by the example.

Since we have not identified the type of application – public facing or internal – we really have no basis to include the data transfer costs. If this were a dedicated internal application that was not intended to face the public internet, then no costs would be incurred for data transfer charges beyond the existing internal infrastructure. If this is a public facing application, then the data transfer charges are very dependent on the details of the application transfers and the external network connections and costs. This is beyond what I am attempting to summarize here, so I will conveniently ignore these costs.

Related to the networking costs that are being ignored are the data transfer costs. Since data transfers in clouds are usually only costly when entering or leaving the cloud, these costs can be lumped in with the network usage costs.

Cost of Cloud:

Using the Simple Monthly Calculator we can determine what the costs for outsourcing the data center to AWS would be.

For the servers, I chose the c3.8xlarge instances which have 32 cores and 60 GB RAM available. They also have 2 320 GB SSD (ephemeral) disks attached for scratch storage. The on-demand hourly cost is $1.68 while the reserved cost is only $0.628, a 63% savings. We will assume that the crack architects at c9.inc planned well and we are able to reserve the needed instances. The 32 cores per instance means that we will need to provision 32 of these instances.

Lets attach 512 GB persistent (EBS) storage to each server to allow application data and software to survive resets. We can do this using general purpose SSD disks. We will use the S3 storage facility for the required 1000 TB of storage.

The results include a cost of $41K/month for servers, $30K/month for storage, and an additional $5K/month for business support.

This is a total of $76K/month for outsourcing the data center.

Cost of Ownership

The article Overall Data Center Costs provides a more detailed investigation of costs, and includes links to even more detailed analysis of private data center costs. Using the pie chart in that article, and making adjustments to simplify my calculations – since I sometimes attempt to do these in my head – I end up with the following ratios:

• 60% - servers (includes storage)
• 30% - infrastructure (facilities, power, cooling, wiring, etc)
• 10% - networking equipment

From this and other articles, I will make the assumption that the servers, storage, and networking equipment will need to be replaced every 3 years. So costs per month can be derived by dividing total cost by 36.

Looking at recent disk costs, I will assume that we can purchase 4 TB drives for $150, or $37.50 / TB. If we configure the disks using RAID5 in groups of 5 disks, then we have a storage efficiency of 80%, resulting in a need for 1.25 x 1000 TB, or 1250 TB. This will cost a total of $47K.

Thus the storage cost for owning the data center will be $1,300/month.

The server costs will be based on obtaining the HP ProLiant DL360P 1U servers with 32 GB RAM. Populating the two sockets in the servers with 8 core Intel Xeon CPUs each will allow 64 cores per server. This will cost about $8,400 per server, or $525 per core.

From the article Failure Rates in Google Data Centers, which is a bit old now, I can see that they are seeing about a 5.5% failure rate per year. This means we can expect one server to fail about every 20 days. This should allow us to provide a very small overhead amount of servers to allow for the downtime until a failed server is replaced. Since I did not account for the other types of failures mentioned, or analyze the actual MTTF or MTTR numbers, I will simply guess that 5% overhead will be enough.

Wow. That's not much. It will assume that we have very good virtualization layer and task management to allow the application to be resilient through these failures. Since we live in the era of big data, this is the case. :) And actually, the application itself will have been sized to allow for redundancy and resilience if it was developed for a possible cloud environment. If the application is based on a Hadoop cluster with built in redundancy for storage and job management, this is indeed the case.

This minimal capacity overhead will result in a need for 1,075 cores. This will require 68 of the 1U servers. We will assume that c9.inc will not want to do any exceptional facility provisioning for power or cooling, so will populate 3 racks with 23 servers each, for a total of 69 servers. The total cost for servers will then be $579,600 to provide 1,104 cores.

Thus the server cost for owning the data center will be $31K/month. The total server and storage comes to about $32K/month.

Using this cost to estimate the infrastructure and networking costs results in the following:

• $32K / month - servers and storage
• $16K / month - infrastructure
• $5K / month - network equipment

In addition to the infrastructure we will need operational support. Using an estimate of one system administrator per 100 servers, we will only need 1 admin for this data center. I will estimate that cost as approximately $8K/month.

The resulting total cost for owning a data center is then $61K/month.

Note that this is approximately 80% of the cost to provision the data center in the cloud.

Breakeven

So. If the cost for provisioning a private data center is less than provisioniing those resources in a cloud, why would we use cloud resources.

Such a good question! :)

Lets find the break even point for building out the private data center. This is the time when the total cost of cloud operation reaches the cost of building the data center. The idea is that when building a private data center, the costs are up front and must be paid at the start of operations, while a cloud cost accrues each month.

For storage, the cloud monthly costs are $30K and $47K is the total cost for the data center.

The storage break even is reached at $47K / $30K = 1.5 months.

For servers, cloud monthly costs are $41K and $1,116K total cost for the data center.

The server break even is reached at $1,116K / $41K = 27 months.

Combining the two, cloud monthly costs are $76K and $2,200K is the total cost for the private data center.

The combined break even is reached at $2,200K / $76K = 29 months.

What?

From the numbers, we can see that a startup like c9.inc will benefit for the first two years of operation by provisioning their application in the cloud. If the anticipated lifetime of the application – or company! – will surpass this time frame, then it makes economic sense to create a private data center. Caution should be used since this involves incurring costs up front which will expose the entire investment to risk of failure as well as the time value of the initial investment (opportunity cost).

Another benefit of the cloud computing environment is where new applications are being developed and tested. These by their nature are small and short in duration, which clearly benefits from outsourcing the resources to the cloud rather than provisioning them for each project.

Some things to note from the above derivation include that storage in the cloud is expensive. If it were not for the data transfer costs, the use of cloud servers with customer disks would be a good architecture. But with costs structured as they are today, the storage costs are part of the overall cloud costs since that avoids the excessive IOPS (data transfer) charges.

Also note that the data center ownership costs were based on an overhead margin of only 5% for the servers. If the application capacity planning includes more margin than this, the relative costs of ownership versus the cloud increases, pushing out the lifetime required to break even by owning the data center. The cost derivations above also do not include any financing costs or opportunity costs, which can be significant for any single project.

Most of these cost increases for ownership can be mitigated by sharing the data center for additional applications and projects.