Why are there so many data centers in New York, Hong Kong, and Tokyo? These urban centers have some of the most expensive real estate in the world. The cost of labor is high. The tax environment is unfavorable. Power costs are high. Construction is difficult to permit and expensive. Urban datacenters are incredibly expensive facilities and yet a huge percentage of the world’s computing is done in expensive urban centers.
One of my favorite examples is the 111 8th Ave data center in New York. Google bought this datacenter for $1.9B. They already have facilities on the Columbia river where the power and land are cheap. Why go to New York when neither is true? Google is innovating in cooling technologies in their Belgium facility where they are using waste water cooling. Why go to New York where the facility is conventional, the power source predominantly coal-sourced, and the opportunity for energy innovation is restricted by legacy design and the lack of real estate available in the area around the facility. It’s pretty clear that 111 8th Ave isn’t going to be wind farm powered. A solar array could likely be placed on the roof but that wouldn’t have the capacity to run the interior lights in this large facility (See I love Solar but … for more on the space challenges of solar power at data center power densities). There isn’t space to do anything relevant along these dimensions.
Google has some of the most efficient datacenters in the world, running on some of the cleanest power sources in the world, and custom engineered from the ground up to meet their needs. Why would they buy an old facility, in a very expensive metropolitan area, with a legacy design? Are they nuts? Of course not, Google is in New York because many millions of Google customers are in New York or nearby.
Companies site datacenters near the customers of those data centers. Why not serve the planet from Iceland where the power is both cheap and clean? When your latency budget to serve customers is 200 msec, you can’t give up ¾ of that time budget on speed of light delays traveling long distances. Just crossing the continent from California to New York is a 74 msec round trip time (RTT). New York to London is 70 msec RTT. The speed of light is unbending. Actually, it’s even worse than the speed of light in that the speed of light in a fiber is about 2/3 of the speed of light in a vacuum (see Communicating Beyond the Speed of Light).
Because of the cruel realities of the speed of light, companies must site data centers where their customers are. That’s why companies selling world-wide, often need to have datacenters all over the world. That’s why the Akamai content distribution network has over 1,200 points of presence world-wide. To serve customers competitively, you need to be near those customers. The reason datacenters are located in Tokyo, New York, London, Singapore and other expensive metropolitan locations is they need to be near customers or near data that is in those locations. It costs considerably to maintain datacenters all over the world but there is little alternative.
Many articles recently have been quoting the Greenpeace open letter asking Ballmer, Bezos and Cook to “go to Iceland”. See for example Letter to Ballmer, Bezos, and Cook: Go to Iceland. Having come many of these articles recently, it seemed worth stopping and reflecting on why this hasn’t already happened. It’s not like company just love paying more or using less environmentally friendly power sources for their data centers.
Google is in New York because it has millions of customers in New York. If it were physically possible to serve these customers from an already built, hyper efficient datacenter like Google Dalles, they certainly would. But that facility is 70 msec round trip away from New York. What about Iceland? Roughly the same distance. It simply doesn’t work competitively. Companies build near their users because physics of the speed of light is unbending and uncaring.
So, what can we do? It turns out that many workloads are not latency sensitive. The right strategy is to house latency sensitive workloads near customers or the data needed at low latency and house latency insensitive workloads optimizing on other dimensions. This is exactly what Google does but, to do that, you need to have many datacenters all over the world so the appropriate facility can be selected on a workload-by-workload basis. This isn’t a practical approach for many smaller companies with only 1 or 2 datacenters to choose from.
This is another area where cloud computing can help. Cloud computing can allow mid-sized and even small companies to have many different datacenters optimized for different goals all over the world. Using Amazon Web Services, a company can house workloads near customers in Singapore, Tokyo, Brazil, and Ireland to be close to their international customers. Being close to these customers makes a big difference in the overall quality of customer experience (see: The Cost of Latency for more detail on how much latency really matters). As well as allowing a company to cost effectively have an international presence, cloud computing also allows companies to make careful decisions on where they locate workloads in North America. Again using AWS as the example, customers can place workloads in Virginia to serve the east coast or use Northern California to serve the population dense California region. If the workloads are not latency sensitive or is serving customers near the Pacific Northwest, they can be housed in the AWS Oregon region where the workload can be hosted coal free and less expensively than in Northern California.
The reality is that physics is uncaring and many workloads do need to be close to users. Cloud computing allows all companies to have access to datacenters all over the world so they can target individual workloads to the facilities that most closely meet their goals and the needs of their customers. Some computing will have to stay in New York even though it is mostly coal powered, expensive, and difficult to expand. But some workload will run very economically in the AWS West (Oregon) region where there is no coal power, expansion is cheap, and power inexpensive.
Workload placement decisions are more complex than “move to Iceland.”