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Cloud datacenters

With the rise of cloud computing and software or infrastructure as a service, we can say that the datacenters cloud providers build the cloud datacenters. Because of the number of servers they house, they generally demand a much, much higher capacity of power, cooling, network speed, and feed than any enterprise datacenter. In fact, the cloud datacenters are so big they are typically built close to power sources where they can get the cheapest rate for power, without losing too much during transportation of the power. They can also be creative when it comes to cooling down where the datacenter might be build in a generally cold climate, so they can just open the doors and windows to keep the server running at a safe temperature. Any search engine can give you some of the astounding numbers when it comes to the science of building and managing these cloud datacenters for the likes of Amazon, Microsoft, Google, and Facebook:

Utah Data Center (source: https://en.wikipedia.org/wiki/Utah_Data_Center)

The services that the servers at datacenters need to provide are generally not cost efficient to be housed in any single server. They are spread among a fleet of servers, sometimes across many different racks to provide redundancy and flexibility for service owners. The latency and redundancy requirements put a tremendous amount of pressure on the network. The number of interconnection equates to an explosive growth of network equipment; this translates into the number of times these network equipment need to be racked, provisioned, and managed.

CLOS Network (source: https://en.wikipedia.org/wiki/Clos_network)

In a way, the cloud datacenter is where network automation becomes a necessity. If we follow the traditional way of managing the network devices via a Terminal and command-line interface, the number of engineering hours required would not allow the service to be available in a reasonable amount of time. This is not to mention that human repetition is error prone, inefficient, and a terrible waste of engineering talent.

The cloud datacenter is where the author started his path of network automation with Python a number of years ago, and never looked back since.

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