In current Data Center Networks (DCNs), Equal- Cost MultiPath (ECMP) is used as the de-facto routing protocol. However, ECMP does not differentiate between short and long flows, the two main categories of flows depending on their duration (lifetime). This issue causes hot-spots in the network, affecting negatively the Flow Completion Time (FCT) and the throughput, the two key performance metrics in data center networks. Previous work on load balancing proposed solutions such as splitting long flows into short flows, using per-packet forwarding approaches, and isolating the paths of short and long flows. We propose DiffFlow, a new load balancing solution which detects long flows and forwards packets using Random Packet Spraying (RPS) with help of SDN, whereas the flows with small duration are forwarded with ECMP by default. The use of ECMP for short flows is reasonable, as it does not create the out-of-order problem; at the same time, RPS for long flows can efficiently help to load balancing the entire network, given that long flows represent most of the traffic in DCNs. The results show that our DiffFlow solution outperforms both the individual usage of either RPS or ECMP, while the overall throughput achieved is maintained at the level comparable to RPS.
In spite of the ideas presented in these works, current data centers are slow to adopting new schemes, especially when they require modification of legacy network elements, or transport protocols. For that reason, our focus is on the solution deploying SDN, which is well-adopted in data centers. In our proposal, OpenFlow switches are essential to differentiate the flows. In our model, we use the packet sampling technique, also widely adopted in practice, such as sFlow @cite_0 . The main advantage of this method compared to others is that the sampling of packets is only realized locally in the TORs OpenFlow switches. Therefore, the SDN controller is not responsible for this task, assuring a high scalability for bigger networks, which is addressing an important concern of centralized SDN controllers. Packet sampling is however not the only detection technique that can be used, and our solution is open to other methods, for instance, per-flow statistics as in Hedera @cite_5 , or end-host based monitoring as in Mahout @cite_7 . On the other hand, it is important to underline that DiffFlow can coexist with traditional TCP protocol or newer versions, since its procedure is transparent to upper layer protocols.
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Datacenter networks provide high path diversity for traffic between machines. Load balancing traffic across these paths is important for both, latency- and throughput-sensitive applications. The standard load balancing techniques used today obliviously hash a flow to a random path. When long flows collide on the same path, this might lead to long lasting congestion while other paths could be underutilized, degrading performance of other flows as well. Recent proposals to address this shortcoming incur significant implementation complexity at the host that would actually slow down short flows (MPTCP), depend on relatively slow centralized controllers for rerouting large congesting flows (Hedera), or require custom switch hardware, hindering near-term deployment (DeTail). We propose FlowBender, a novel technique that: (1) Load balances distributively at the granularity of flows instead of packets, avoiding excessive packet reordering. (2) Uses end-host-driven rehashing to trigger dynamic flow-to-path assignment. (3) Recovers from link failures within a Retransmit Timeout (RTO). (4) Amounts to less than 50 lines of critical kernel code and is readily deployable in commodity data centers today. (5) Is very robust and simple to tune. We evaluate FlowBender using both simulations and a real testbed implementation, and show that it improves average and tail latencies significantly compared to state of the art techniques without incurring the significant overhead and complexity of other load balancing schemes. OpenFlow is one of the most famous protocols for controller-to-switch communications in software-defined networking (SDN), commonly seen as a promising way towards future Internet. Understanding the performance and limitation of OpenFlow-based SDN is a prerequisite of its deployments. To achieve this aim, this paper proposes a novel analytical performance model of OpenFlow networks based on queueing theory. After depicting a typical network scenario of OpenFlow deployments, we model the packet forwarding of its OpenFlow switches and the packet-in message processing of its SDN controller respectively as the queueing systems MX M 1 and M G 1. Subsequently, we build a queueing model of OpenFlow networks in terms of packet forwarding performance, and solve its closed-form expression of average packet sojourn time and the corresponding probability density function. Finally, the numerical analysis is carried out to evaluate our proposed performance model with different parameter values. Furthermore, our controller model is contrasted with the classical one by utilizing the popular benchmark Cbench. Experimental results indicate that our controller model provides a more accurate approximation of SDN controller performance. Software defined networks (SDNs) introduced the concept of decoupling control and data planes which is a paradigm shift. The OpenFlow protocol is one of a number of technologies that enables this decoupling and, in effect, commodifies network equipment. As of now, there is still limited work that has been done towards modeling the transit delay across OpenFlow switches experienced by network traffic. In this work we develop a stochastic model for the path latency in Open vSwitch (used together with a POX controller) based on measurements made in experiments performed on three different platforms which include 1) Mininet, 2) MikroTik RouterBoard 750GL and 3) GENI testbed softswitch. We propose a log-normal mix model (LNMM) and show that it offers a R2 value of greater than 0.90 for most of our experiments. We also demonstrate how the M M 1 models proposed in earlier studies is a poor fit. Modern data center networks are commonly organized in multi-rooted tree topologies. They typically rely on equal-cost multipath to split flows across multiple paths, which can lead to significant load imbalance. Splitting individual flows can provide better load balance, but is not preferred because of potential packet reordering that conventional wisdom suggests may negatively interact with TCP congestion control. In this paper, we revisit this “myth” in the context of data center networks which have regular topologies such as multi-rooted trees. We argue that due to symmetry, the multiple equal-cost paths between two hosts are composed of links that exhibit similar queuing properties. As a result, TCP is able to tolerate the induced packet reordering and maintain a single estimate of RTT. We validate the efficacy of random packet spraying (RPS) using a data center testbed comprising real hardware switches. We also reveal the adverse impact on the performance of RPS when the symmetry is disturbed (e.g., during link failures) and suggest solutions to mitigate this effect.