Flexible datacenters rely on high-bandwidth server-rack fabrics to allocate their distributed computing and storage resources anywhere,anyhow, and anytime demanded. We describe the multicast architecture of a distributed server-rack fabric, which is arranged around a spine-leaf topology and connects 640 Ethernet ports running at 100G. To cope with the immense fabric speed, we resort to hierarchical, tree-based replication, facilitated by specially commissioned fabric-end ports. At each (port-to-port) leg of the tree, a frame copy is forwarded after a request-grant admission phase and is ACKed by the receiver. To save on bandwidth, we use a packet cache in our input-queued switching-nodes, which replicates asynchronously forwarded frames thus tolerating the variable-delay in the admission phase. Because the cache has limited size, we loosely synchronize the multicast subflows to protect the cache from thrashing. We describe our policies for lossy classes, which segregate and provide fair treatment to multicast subflows. Finally, we show that industry-standard Level2 congestion control does not adapt well to one-to-many flows, and demonstrate that the methods that we implement achieve the best performance.

Performance-optimized datacenter networks aim to handle more efficiently the growing East-West intra-cluster traffic of BigData applications. The demanding latency constraints and traffic patterns of these applications expose the inherent bottlenecks of the often oversubscribed datacenter network topologies, favoring in stead the full-bisectional bandwidth fat-trees. And yet their topological benefits may remain unrealized in practical deployments, if such fabrics use single path or flow-level (ECMP hashing) multipath routing. Here we model in detail on Layer 2 the routing performance of modern fat-tree networks using stochastic permutations of bursty traffic. We first analytically simplify and then validate by accurate simulation models that the throughputs for ‘static’ d-mod-k and for ECMP-like multipath routing are 63% and 47%, respectively. We also find that ECMP routing results in a wide spread of link loads under random permutation traffic, which manifests as a 3x throughput reduction for 30% of the flows. Furthermore, ECMP can lead to collisions of mouse and elephant flows, often increasing the flow completion time (FCT) of delay-sensitive flows by a factor of 10. In contrast, packet-based multipath outperforms all the others in this study.

The covariance of complex random variables and processes, when defined consistently with the corresponding notion for real random variables, is shown to be determined by the usual complex covariance together with a quantity called the pseudo-covariance. A characterization of uncorrelatedness and wide-sense stationarity in terms of covariance and pseudo-covariance is given. Complex random variables and processes with a vanishing pseudo-covariance are called proper. It is shown that properness is preserved under affine transformations and that the complex-multivariate Gaussian density assumes a natural form only for proper random variables. The maximum-entropy theorem is generalized to the complex-multivariate case. The differential entropy of a complex random vector with a fixed correlation matrix is shown to be maximum if and only if the random vector is proper, Gaussian, and zero-mean. The notion of circular stationarity is introduced. For the class of proper complex random processes, a discrete Fourier transform correspondence is derived relating circular stationarity in the time domain to uncorrelatedness in the frequency domain. An application of the theory is presented.>