So far, we've looked at compute and we've looked at storage. The third part of Google Cloud infrastructure is networking. Google's high-quality private network, petabit bisectional bandwidth, and Edge points-of-presence are combined using state- of-the-art software- defined networking to deliver a powerful solution. First, the private network. Google has laid thousands of miles of fiber optic cable that crosses oceans with repeaters to amplify optical signals, and as you can see in this amusing GIF, it's shark-proof. Google's data centers around the world are interconnected by this private Google network, which by some publicly available estimates, carries as much as 40 percent of the world's internet traffic everyday. This is the largest network of its kind on Earth and it continues to grow. Second, the petabit bisectional bandwidth. One of the teams we will discuss in this course is a separation of compute and storage. You no longer need to do everything on a single machine or even a single cluster of machines with their own dedicated storage. Why? Well, if you have a fast-enough network, you can perform computations on data located elsewhere like many distributed servers. Google's Jupiter Network can deliver enough bandwidth to allow 100,000 machines to communicate amongst each other. So for any machine to communicate with any other machine in the data center at over 10 gigabits per second. This full duplex bandwidth means that locality within the cluster is not important. If every machine can talk to every other machine at 10 Gbps, racks don't matter for data analytics and machine learning. But you need to ingest data probably from around the world. You need to serve out the results of your analytics and predictions, perhaps to users who are all around the world. This is where Edge points of presence come in. The network, Google's Network, interconnects with the public Internet at more than 90 internet exchanges and more than 100 points of presence worldwide. When an Internet user sends traffic to a Google resource, Google responds to the user's request from an Edge network location that will provide the lowest delay or latency. Google's Edge caching network places content close to end-users to minimize latency. Your applications in GCP, like your machine learning models, can take advantage of this Edge network too.