As virtualization, cloud computing, and distributed cloud become more pervasive in the data center, a shift in the traditional three-tier networking model is taking place. This shift addresses simplicity and scalability.
The traditional core-aggregate-access model is efficient for north/south traffic that travels in and out of the data center. This model is usually built for redundancy and resiliency against failure. However, the Spanning Tree Protocol (STP) typically blocks 50 percent of the critical network links to prevent network loops, which means 50 percent of the maximum bandwidth is wasted until something fails.
A core-aggregate-access architecture is still widely used for service-oriented traffic that travels north/south. However, the trends in traffic patterns are changing with the types of workloads. In today’s data centers east/west or server-to-server traffic is common. If the servers in a cluster are performing a resource-intensive calculation in parallel, unpredictable latency or lack of bandwidth are undesirable. Powerful servers that perform these calculations can attempt to communicate with each other, but if they cannot communicate efficiently because of a bottleneck in the network architecture, wasted capital expenditure results.
One way to solve the problem is to create a leaf-and-spine architecture, also known as a distributed core.
A leaf-and-spine architecture has two main components: spine switches and leaf switches.
Spine switches can be thought of as the core, but instead of being a large, chassis-based switching platform, the spine consists of many high-throughput Layer 3 switches with high port density.
Leaf switches can be treated as the access layer. Leaf switches provide network connection points for servers and uplink to the spine switches.
Every leaf switch connects to every spine switch in the fabric. No matter which leaf switch a server is connected to, it always has to cross the same number of devices to get to another server (unless the other server is located on the same leaf). This design keeps the latency down to a predictable level because a payload has to hop only to a spine switch and another leaf switch to get to its destination.
Instead of relying on one or two large chassis-based switches at the core, the load is distributed across all spine switches, making each individual spine insignificant as the environment scales out.
Several factors, including the following, affect scalability.
Number of racks that are supported in a fabric.
Amount of bandwidth between any two racks in a data center.
Number of paths a leaf switch can select from when communicating with another rack.
The total number of available ports dictates the number of racks supported in a fabric across all spine switches and the acceptable level of oversubscription.
Different racks might be hosting different types of infrastructure. For example, a rack might host filers or other storage systems, which might attract or source more traffic than other racks in a data center. In addition, traffic levels of compute racks (that is, racks that are hosting hypervisors with workloads or virtual machines) might have different bandwidth requirements than edge racks, which provide connectivity to the outside world. Link speed as well as the number of links vary to satisfy different bandwidth demands.
The number of links to the spine switches dictates how many paths are available for traffic from this rack to another rack. Because the number of hops between any two racks is consistent, equal-cost multipathing (ECMP) can be used. Assuming traffic sourced by the servers carry a TCP or UDP header, traffic distribution can occur on a per-flow basis.