Quantized congestion notification (QCN), discussed in IEEE 802.1Qau, is one of the most promising Layer 2 congestion control methods for data center networks. Because data center networks have fundamentally symmetric structures and links are designed to have high link utilization, data center flows often pass through multiple bottleneck links. QCN reduces its transmission rate in a probabilistic manner with each congestion notification feedback reception, which might cause excessive regulation of the transmission rate in a multiple-bottleneck case because each bottleneck causes congestion feedbacks. We have already proposed QCN with bottleneck selection (QCN/BS) for multicast communications in data center networks. Although QCN/BS was originally proposed for multicast communications, it can also be applied to unicast communications with multiple bottleneck points. QCN/BS calculates the congestion level for each switch based on feedback from the switch and adjusts its transmission rate to the worst congestion level. In this paper, we preliminarily evaluate QCN/BS in unicast communications with multiple tandem bottleneck points. Our preliminary evaluation reveals that QCN/BS can resolve the excessive rate regulation problem of QCN but has new fairness problems for long-hop flows. To resolve this, we propose a new algorithm that integrates QCN/BS and our already proposed Adaptive BC_LIMIT. In Adaptive BC_LIMIT, the opportunities for rate increase are almost the same for all flows even if their transmission rates differ, enabling an accelerated convergence of fair rate allocation among flows sharing a bottleneck link. The integrated algorithm is the first congestion control mechanism that takes into account unicast flows passing through multiple tandem bottleneck points based on QCN. Furthermore, it does not require any modifications of switches used in QCN. Our simulation results show that our proposed integration of QCN/BS and Adaptive BC_LIMIT significantly mitigates the fairness problem for unicast communications with multiple bottleneck points in data center networks.

Data center network is composed of high-speed Ethernet extended in a limited area of a data center building, so its RTT is extremely small of µsec order. In order to regulate data center network delay large part of which is queuing delay, QCN is proposed for layer 2 congestion control in IEEE 802.1Qau. QCN controls transmission rate of the sender by congestion feedback from a congested switch. QCN adopts probabilistic feedback transmission to reduce the control overhead. When the number of flows through a bottleneck link increases, some flows might receive no feedback even in congestion phase due to probabilistic feedback transmission. In this situation, queue length might be significantly fluctuated. In this paper, we propose a new delay-based congestion detection and control method. Our proposed delay-based congestion control is cooperated with the conventional QCN so as to detect and react congestion not detected by QCN.

In data center networks, group communication is currently playing an important role and multicast communications is an effective way to support group communication for large numbers of virtual machines. Layer-2 congestion control named QCN (Quantized Congestion Notification) has been proposed to realize the high reliability required by LAN/SAN integration in data center networking. Our preliminary evaluation in this paper shows that a multicast flow suffers lower throughput than unicast flows when conventional QCN is applied in a naive manner. This is because a sending device receives congestion feedback from multiple locations on a multicast tree and decreases transmission rate accordingly. To counter this throughput degradation of multicast flows, we propose a new Layer 2 congestion control algorithm in multicast environment, Quantized Congestion Notification with Bottleneck Selection (QCN/BS). In QCN/BS, the switch in the worst congestion level is selected and the transmission rate of the sending device is calculated exclusively according to feedback from the selected switch. Simulation results show that when conventional QCN is used, a multicast flow experiences lower and more severely unfair throughput than a unicast flow. The proposed QCN/BS resolves this problem.

In large-scale data centers, two types of network are implemented: local area networks (LANs) and storage area networks (SANs). To achieve simple network management, integration of these two networks by Ethernet technology is of great interest. A SAN requires a significantly low frame loss rate. To integrate LANs and SANs, a multi-hop Ethernet configuration is generally used, and congestion may occur in traffic hot spots. Therefore, layer-2 congestion control that prevents frame loss in multi-hop Ethernet, Quantized Congestion Notification (QCN), is now discussed in IEEE 802.1Qau. In this paper, we evaluate QCN's throughput performance and reveal a technical problem with fairness among active flows. We also propose Adaptive BC_LIMIT for QCN where BC_LIMIT is adaptively decided according to current transmission rate of flows. Simulation results show that our proposed method significantly improves fairness among QCN flows.