Ultra-large feedback-based switch implementation for data center networks

The feedback-based switch [1] is a load-balanced switch consisting of two stages of crossbar switch fabrics. Each crossbar switch fabric is configured according to a predetermined and periodic seuqence of switch configurations. This eliminates the need for a central scheduler and makes it an excellent candidate for connecting tens of thousands of servers in a data center. But implementing feedback-based switch at the data center scale is challenging. A major issue is that crossbar switch fabrics are not scalable. In this paper, two scalable implementations of the feedback-based switch, dualbanyan network and Clos-banyan network, are proposed. With the dual-banyan implementation, the two crossbar switch fabrics are replaced by two modular banyan networks. To further reduce the dual-banyan network complexity, we merge the last stage of switch modules in the first banyan, the middle-stage ports, and the first stage of switch modules in the second banyan, to form a single stage of shared-memory switch modules. We call the resulting network Clos-banyan. We prove that the feedback-based switch implemented using either dual-banyan or Clos-banyan can realize all the switch configurations required by the original crossbarbased implementation, but at a much lower hardware cost. Since the packet scheduling algorithm is independent of how switch configurations are being realized, different implementations of the feedback-based switch provide the same excellent delaythroughput performance, as that already reported in [1].