High performance multipath routing for datacenters

Abstract

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.