Quantized Congestion Notification Research Articles

Nonlinear Control Analysis of Quantized Congestion Notification in Data Center Networks

In data center networks, quantized congestion notification (QCN) has been ratified as the standard layer 2 congestion control mechanism. Since the congestion control algorithm of QCN conducts nonlinear switching between the rate increasing and decreasing stages, it is very difficult to analyze QCN theoretically. Previous works derive the stability condition of QCN using tools from linear control theory. However, the tools cannot reveal the reason why the queue at a congested link oscillates persistently when QCN is enabled. In this paper, we use the describing function method from nonlinear control theory to analyze queue oscillation. We show that unsuitable network configurations like more flows, a higher link capacity and a larger link delay will cause more severe oscillation of the queue. In particular, the impacts of the QCN parameters, such as the sampling probability and the constant in the active increase phase in QCN, on the oscillation magnitude of the queue are also analyzed. The simulation results validate the conclusion from our theoretical analysis well.

P4QCN: Congestion Control Using P4-Capable Device in Data Center Networks

Modern data centers aim to offer very high throughput and ultra-low latency to meet the demands of applications such as online intensive services. Traditional TCP/IP stacks cannot meet these requirements due to their high CPU overhead and high-latency. Remote Direct Memory Access (RDMA) is an approach that can be designed to meet this demand. The mainstream transport protocol of RDMA over Ethernet is RoCE (RDMA over Converged Ethernet), which relies on Priority Flow Control (PFC) within the network to enable a lossless network. However, PFC is a coarse-grained protocol which can lead to problems such as congestion spreading, head-of-the-line blocking. A congestion control protocol that can alleviate these problems of PFC is needed. We propose a protocol, called P4QCN for this purpose. P4QCN is a congestion control scheme for RoCE and it is an improved Quantized Congestion Notification (QCN) design based on P4, which is a flow-level, rate-based congestion control mechanism. P4QCN extends the QCN protocol to make it compatible with IP-routed networks based on a framework of P4 and adopts a two-point algorithm architecture which is more effective than the three-point architecture used in QCN and Data Center QCN(DCQCN). Experiments show that our proposed P4QCN algorithm achieves the expected performance in terms of latency and throughput.

Analysing and improving convergence of quantized congestion notification in Data Center Ethernet

Quantized Congestion Notification (QCN) has been approved as the standard congestion management mechanism for the Data Center Ethernet (DCE). However, lots of work pointed out that QCN suffers from the problem of unfairness among different flows. In this paper, we found that QCN could achieve fairness, merely the convergence time to fairness is quite long. Thus, we build a convergence time model to investigate the reasons of the slow convergence process of QCN. We validate the precision of our model by comparing with experimental data on the NetFPGA platform. The results show that the proposed model accurately well characterizes the convergence time to fairness of QCN. Based on the model, the impact of QCN parameters, network parameters, and QCN variants on the convergence time is analysed in detail. Results indicate that the convergence time of QCN can be decreased if sources have the same rate increase probability or the rate increase step becomes larger at steady state. Enlightened by the analysis, we proposed a mechanism called QCN-T, which replaces the original Byte Counter and Timer at sources with a single modified Timer to reduce the convergence time. Finally, evaluations show great improvements of QCN-T in both convergence and stability.

Modelling and evaluation of QCN using coloured petri nets

Congestion management capability is an important requirement for any quality of services (QoS) networking technology. Therefore, IEEE 802.1Qau has proceeded a congestion notification project whose aim is to develop a Carrier Ethernet congestion control scheme at Layer 2 for a data center network. A data center network should be efficient and robust to handle the growing demands of cloud computing. A congestion control scheme is a key component of data transport in this kind of network. The quantized congestion notification (QCN) scheme is a congestion management scheme, which has been standardized and designed to be implemented in the Carrier Ethernet. Coloured Petri net (CPN) is a modelling technique used with the Petri nets modelling tool. This technique is exploited to model and simulate the QCN in order to help understand the QCN scheme and derive performance measures. This paper proposes a formal model for the QCN scheme. Then, we verify the implementation of our model by simulations. Thus, the QCN queue and rate performances are evaluated using the CPN tools. Moreover, an improved model called TAI-QCN is proposed in order to enhance the QCN scheme.

FSQCN: Fast and simple quantized congestion notification in data center ethernet

Currently, Quantized Congestion Notification (QCN) has been accepted as the standard layer 2 congestion control protocol in Data Center Ethernet. In this study, we find that QCN has two drawbacks. First, the incomplete binary search of QCN may fail to find the proper sending rate, leading to the complicate supplement mechanisms of QCN. Accordingly, the cost of hardware implementation is increased. Second, in face of unknown network environments, the rate setting of QCN is inconsistent. More specifically, the sending rate is set to be the link rate at initialization, while QCN additively probes for extra bandwidth in face of dynamic network. Consequently, QCN may spend much time on acquiring the spare bandwidth if its sending rate is increased from a low value. To address these problems, we propose the Fast and Simple QCN (FSQCN), following the same framework as QCN. FSQCN complements the binary search and removes other complicate supplement mechanisms in QCN. Thus, FSQCN is much simpler and more efficient than QCN. Moreover, FSQCN resets the sending rate to the link rate explicitly when the switch detects spare bandwidth. Extensive simulation experiments validate that FSQCN controls the queue length well like QCN, and responds faster than QCN, especially in face of the large number of burst short flows in data center networks.

Unbiased Quantized Congestion Notification for Scalable Server Fabrics

Ethernet is the predominant layer-2 networking technology in the datacenter, and it is evolving into an economical alternative for high-performance computing clusters. Ethernet traditionally drops packets in the event of congestion, but IEEE introduced lossless class services to enable the convergence of storage and IP networks. Losslessness is a simple, well-known concept, but its application in datacenters is hampered by the fear of ensuing saturation trees. In this article, the authors aim to accelerate the deployment of Quantized Congestion Notification (QCN). In particular, they first eliminate the intrinsic unfairness of QCN under typical fan-in scenarios by installing the congestion points at inputs, instead of at outputs as standard QCN does. They then demonstrate that QCN at input buffers cannot always discriminate between culprit and victim flows. To overcome this limitation, they propose a novel QCN-compatible marking scheme called occupancy sampling. They have implemented these methods in a server-rack fabric with 640 100-Gigabit Ethernet ports.

Enhanced ethernet congestion management scheme for multicast traffic

AbstractThe Quantized Congestion Notification (QCN) is a Layer 2 congestion control scheme for Carrier Ethernet data center networks. The QCN has been standardised as an IEEE 802.1Qau Ethernet Congestion Notification standard. This paper report a results of a QCN study with multicast traffic and proposes an enhancement to the QCN. In fact, in order to be able to scale up, the feedback implosion problem has to be solved. Therefore, we resorted to the representative technique, which uses a selected congestion point (i.e. the overloaded queue in a switch), to provide timely and accurate feedback on behalf of the congested switches in the path of multicast traffic. This paper evaluates the rate variation, the feedback overhead, the loss rate, the stability, the fairness and the scalability performance of the standard QCN with multicast traffic and the enhanced QCN for multicast traffic. This paper also compares their performance criteria. The evaluation results show that the enhanced proposition of the QCN for multicast traffic gives better results than the standard QCN with multicast traffic. Indeed, the feedback implosion problem is settled by decreasing remarkably the feedback rate. Copyright © 2016 John Wiley & Sons, Ltd.

Performance evaluation of legacy QCN for multicast and multiple unicast traffic transmission

SummaryA multicast congestion control scheme is an interesting feature to control group communication applications such as teleconferencing tools and information dissemination services. This paper addresses a comparison between multiple unicast and multicast traffic congestion control for Carrier Ethernet. In this work, we proposed to study the quantized congestion notification (QCN), which is a layer 2 congestion control scheme, in the case of multicast traffic and multiple unicast traffic. Indeed, the QCN has recently been standardized as the IEEE 802.1Qau Ethernet Congestion Notification standard. This scheme is evaluated through simulation experiments, which are implemented by the OMNeT++ framework. This paper evaluates the reaction point start time congestion detection, feedback rate, loss rate, stability, fairness and scalability performance of the QCN for multicast traffic transmission and multiple unicast traffic transmission. This paper also draws a parallel between QCN for multicast traffic transmission and that for multiple unicast traffic transmission. Despite the benefit of integrating the multicast traffic, results show that performance could degrade when the network scales up. The evaluation results also show that it is probable that the feedback implosion problem caused by the bottlenecks could be solved if we choose to set the queue parameter Qeq threshold value at a high value, 75% of the queue capacity for instance. Copyright © 2016 John Wiley & Sons, Ltd.

Analysis on Buffer Occupancy of Quantized Congestion Notification in Data Center Networks

Analysis on Buffer Occupancy of Quantized Congestion Notification in Data Center Networks

Sliding Mode Congestion Control for Data Center Ethernet Networks

Recently, Ethernet is enhanced as the unified switch fabric of data centers, called data center Ethernet. One of the indispensable enhancements is end-to-end congestion management, and currently quantized congestion notification (QCN) has been ratified as the corresponding standard. However, our experiments show that QCN suffers from large oscillations of the queue length at the bottleneck link such that the buffer is emptied frequently and accordingly the link utilization degrades, with certain system parameters and network configurations. This phenomenon is corresponding to our theoretical analysis result that QCN fails to enter into the sliding mode motion (SMM) pattern with certain system parameters and network configurations. Knowing the drawbacks of QCN and realizing the advantage that congestion management system is insensitive to the changes of parameters and network configurations in the SMM pattern, we present sliding mode congestion control (SMCC), which can enter into the SMM pattern under any conditions. SMCC is simple, stable, fair, has short response time, and can be easily used to replace QCN because both of them follow the framework developed by the IEEE 802.1Qau work group. Experiments on the NetFPGA platform show that SMCC is superior to QCN, especially when traffic pattern and network states are variable.

Phase Plane Analysis of Quantized Congestion Notification for Data Center Ethernet

Currently, Ethernet is being enhanced to become the unified switch fabric in data centers. With the unified switch fabric, the cost on redundant devices is reduced, while the design and management of data center networks are simplified. Congestion management is one of the indispensable enhancements on Ethernet, and Quantized Congestion Notification (QCN) has just been ratified as the formal standard. Though QCN has been investigated for several years, there exist few in-depth theoretical analyses on QCN. The most possible reason is that QCN is heuristically designed and involves the property of variable structure. The classic linear analysis method is incapable of handling the segmented nonlinearity of the variable structure system. In this paper, we use the phase plane method, which is suitable for systems of segmented nonlinearity, to analyze the QCN system. The overall dynamic behaviors of the QCN system are presented, and the sufficient conditions for the stable QCN system are deduced. These sufficient conditions serve as guidelines toward proper parameters setting. Moreover, we find that the stability of QCN is mainly promised by the sliding mode motion, which is the underlying reason for QCN's stable queue shown in numerous simulations and experiments. Experiments on the NetFPGA platform verify that the analytical results can explain the complex behaviors of QCN.

QCN/DC: Quantized Congestion Notification with Delay-Based Congestion Detection in Data Center Networks

QCN/DC: Quantized Congestion Notification with Delay-Based Congestion Detection in Data Center Networks

Multicast Congestion Control with Quantized Congestion Notification in Data Center Networks

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.

Fair Quantized Congestion Notification in Data Center Networks

Quantized Congestion Notification (QCN) has been developed for IEEE 802.1Qau to provide congestion control at the Ethernet Layer or Layer 2 in data center networks (DCNs) by the IEEE Data Center Bridging Task Group. One drawback of QCN is the rate unfairness of different flows when sharing one bottleneck link. In this paper, we propose an enhanced QCN congestion notification algorithm, called fair QCN (FQCN), to improve rate allocation fairness of multiple flows sharing one bottleneck link in DCNs. FQCN identifies congestion culprits through joint queue and per flow monitoring, feedbacks individual congestion information to each culprit through multi-casting, and ensures convergence to statistical fairness. We analyze the stability and fairness of FQCN via Lyapunov functions and evaluate the performance of FQCN through simulations in terms of the queue length stability, link throughput and rate allocations to traffic flows with different traffic dynamics under three network topologies. Simulation results confirm the rate allocation unfairness of QCN, and validate that FQCN maintains the queue length stability, successfully allocates the fair share rate to each traffic source sharing the link capacity, and enhances TCP throughput performance in the TCP Incast setting.

Improvement of Flow Fairness in Quantized Congestion Notification for Data Center Networks

Improvement of Flow Fairness in Quantized Congestion Notification for Data Center Networks