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.
@cite_0 @cite_5 @cite_7
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This memo defines InMon Coporation's sFlow system. sFlow is a technology for monitoring traffic in data networks containing switches and routers. In particular, it defines the sampling mechanisms implemented in an sFlow Agent for monitoring traffic, the sFlow MIB for controlling the sFlow Agent, and the format of sample data used by the sFlow Agent when forwarding data to a central data collector. Today's data centers offer tremendous aggregate bandwidth to clusters of tens of thousands of machines. However, because of limited port densities in even the highest-end switches, data center topologies typically consist of multi-rooted trees with many equal-cost paths between any given pair of hosts. Existing IP multipathing protocols usually rely on per-flow static hashing and can cause substantial bandwidth losses due to long-term collisions. In this paper, we present Hedera, a scalable, dynamic flow scheduling system that adaptively schedules a multi-stage switching fabric to efficiently utilize aggregate network resources. We describe our implementation using commodity switches and unmodified hosts, and show that for a simulated 8,192 host data center, Hedera delivers bisection bandwidth that is 96 of optimal and up to 113 better than static load-balancing methods.