Conventionally, networks in data centers, High Performance Computing (HPC), and the like are built with rigidly structured architectures. Some examples known in data center networks are Fat Tree (Clos), Dragonfly, Slim Fly, and B-Cube. Specifically, a Fat Tree or Clos network is frequently used in modern data centers. This structured network architecture is easy to visualize, can be built from smaller building blocks, provides high bisectional bandwidth, etc. Further, the number of hops is at most 4 in a 3-layer network, traffic is easily routed with Equal-Cost Multi-Path routing (ECMP) and is resilient to failure, and network can be scaled further at the cost of additional layers. At the same time, structured networks suffer from some well-known problems. First, there is increased latency due to many hops, especially as the number of layers grows with structured network architectures. High network loads can produce filled switch buffers, increasing latency. Second, structured network architectures are deployed in discrete implementation sizes, and higher layer ports may go unused in an underfilled network. FIG. 1 illustrates an exemplary three-layer leaf-spine folded-Clos network 10 with various switches 12 interconnecting servers 14. Of note, the Clos network 10 is used to draw comparisons with the systems and methods described herein. As an example, with the three-layer (L=3) Clos network 10 using k-port switch, the following relationships between port count and server and switch counts exist: k=24→servers=3456, switches=720; k=32→servers=8192, switches=1280; k=64→servers=65536, switches=5120. For Fat Tree Computations (k-port switches, L switching layers): the number of layers required: L=log(Nserv/2)/log(k/2)˜log(Nserv)/log(k); the number of servers: Nserv=k*(k/2)*(k/2) . . . =2*(k/2)^L. The total switch count is: Nswitch=(2L−1)*(k/2)^(L−1).
Third, structured network architectures have difficulty in horizontal scaling by requiring multiple layers. Horizontal scaling is explained as follows. In general, hardware devices such as Application Specific Integrated Circuits (ASICs) are port limited by available pins. This means bandwidth can increase, but usually, most increases are achieved by increasing port speeds such as 25G to 56G, i.e., port counts are difficult to increase. However, port counts determine horizontal fan-out capability such as in the Clos network 10. Therefore, network horizontal scale growth will eventually face problems in terms of network layer increases. Each layer requires interconnect, which requires high power backplanes and/or expensive optics. Fourth, structured network architectures are susceptible to cluster-packing problems which confine jobs within clusters reducing latency and improving efficiency. However, the processor (CPU), storage, etc. resources in the cluster must then be sized to anticipate large loads which can often be underutilized.
In contrast to structured network architectures, a purely random approach, while overcoming some of the aforementioned disadvantages, has issues in construction, maintenance, and management. First, a single randomizer device (for an entire network) is difficult to construct, manage, and repair. Second, there are requirements to preserve randomized interconnection, but also allow spatial separation for data center reliability. Third, fiber cabling is unmanageable, and finally, fourth, random networks are difficult to visualize and administer.