Cloud networks support workloads with diverse characteristics. A key challenge facing cloud providers is how to meet the stringent performance and security needs of these diverse applications running over a shared infrastructure. To address this challenge, some providers build for peak capacity or even build dedicated clusters with specialized networks, which may be under-utilized at times. We propose a virtualization approach that customizes data center network resources to the needs of applications. Our approach is based on slicing data center network resources on-demand and customizing these slices to target workloads. Such slices can grow or shrink dynamically and programmatically based on workload demands. This elasticity provides a more efficient solution over building dedicated clusters. In our approach, a slice can be customized to a given set of workloads with similar security and performance requirements carved out of the underlying network. It leverages a software-defined underlay network controller and segment routing for fine-grained path control and service chaining. We have implemented a prototype of our fabric virtualization solution based on network slicing. In this paper, we first present the architecture of our prototype. Second, we present empirical results of slice provisioning times in networks of varying sizes and switch operating systems. Empirical results indicate that our prototype can support slice provisioning in the order of tens to hundreds of seconds and can meet the provisioning requirements of production networks.

Performance and scalability of cloud applications are heavily dependent on those same characteristics in the underlying network. Several factors complicate cloud network evaluation. Most notable are 1) large scale, 2) a practically infinite set of application workloads, 3) a large set of potential metrics, 4) a large variety of available configurations within and across clouds and 5) a lack of visibility into the underlying network. There is a tremendous need for a cloud network evaluation methodology that overcomes these challenges, that is practical to apply and provides predictive results. We describe our work to develop such a methodology for cloud network evaluation and a tool (Euclid) that automates its application. Euclid leverages a set of widely used traffic generators to evaluate throughput and latency across a cloud network between endpoint pairs. Evaluation of these micro benchmarks between and among different sets of endpoints is coordinated to reflect several well-known application traffic patterns. Large sets of results are distilled into a tractable set of metrics and associated graphs that facilitate consumption of the results by cloud operators and users. We report results from applying Euclid to seventeen cloud configurations that span three public and one private cloud. The results demonstrate the practicality of our approach and highlight some interesting differences across clouds.

In today's datacenters handling big data and for exascale computers of tomorrow, there is a pressing need for high-radix switches to economically and efficiently unify the computing and storage resources that are dispersed across multiple racks. In this paper, we present SCOC, a switch architecture suitable for economical IC implementation that can efficiently replace crossbars for high-radix switch nodes. SCOC is a multi-stage bufferless network with O(N2/m) cost, where m is a design parameter, practically ranging between 4-16. We identify and resolve more than five fairness violations that are pertinent to hierarchical scheduling. Effectively, from a performance perspective, SCOC is indistinguishable from efficient flat crossbars. Computer simulations show that it competes well or even outperforms flat crossbars and hierarchical switches. We report data from our ASIC implementation at 32 nm of a SCOC 136×136 switch, with shallow buffers, connecting 25 Gb/s links. In this first incarnation, SCOC is used at the spines of a server-rack, fat-tree network. Internally, it runs at 9.9 Tb/s, thus offering a speedup of 1.45 ×, and provides a fall-through latency of just 61 ns.

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.