Explicit Congestion Notification Research Articles

A multi-queue-based ECN marking strategy for multi-class QoS guarantee in programmable networks

Currently, network applications are experiencing explosive growth, and various types of network applications are showing a trend of varied demands for quality of network service. However, the existing Explicit Congestion Notification (ECN) marking methods have not taken into account the diversified Quality of Service (QoS) requirements of network applications. This article introduces a multi-queue ECN marking strategy targeting multiple QoS guarantees. The strategy utilizes virtual queues and dynamic weighted round-robin scheduling to achieve traffic partitioning in a programmable data plane. It constructs a multi-queue, multi-class QoS queuing model based on the QoS requirements of different traffic and network conditions. The model is solved by real-time to obtain the ECN marking thresholds and round-robin weights for different queues, in order to achieve dynamic QoS requirements of different network applications. We implemented this strategy in Mininet and BMv2, and compared it with DCQCN, P4QCN, and TCN. The experimental results indicate that this policy demonstrates good performance in terms of queue length, RTT, and throughput, while also ensuring fairness between traffics. Results of the experiment indicate that the proposed approach is superior to DCQCN and P4QCN in the field of performance fluctuation and rapid feedback, and it exhibits notable advantages over TCN, and also ensures the fairness of traffic.

Fairness Improvement Method Using Explicit Congestion Notification for QUIC

Fairness Improvement Method Using Explicit Congestion Notification for QUIC

Micro-Burst Aware ECN in Multi-Queue Data Centers: Algorithm and Implementation

With the development of multi-queue data centers, various efforts have leveraged Explicit Congestion Notification (ECN) to achieve high throughput and low latency for data communication. However, one of the deep-seated problems is that the micro-burst traffic in networks could cause the instantaneous queue length to exceed the ECN threshold, leading to significant mismarkings of ECN. Such mismarkings could further lead to severe network performance degradation. In this paper, we propose Micro-Burst aware ECN (MBECN+) to mitigate this issue. MBECN+ is essentially a novel scheme that decouples ECN threshold setting and ECN marking. First, it finds a more appropriate ECN threshold on a per-queue basis to eliminate the spurious congestion signals caused by micro burst traffic. Second, MBECN+ consists of a double-threshold ECN marking scheme that it marks the packets with ECN in a finer grain. Considering the queue backlog caused by micro-burst traffic, it marks the packets when they are dequeuing, instead of enqueuing. Furthermore, we analyze the feasibility of implementing MBECN+ on commodity layer-3 multi-queue switches. Through testbed experiments and large scale simulations, we demonstrate that MBECN+ can improve the throughput by up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 20% and reduce FCT (flow completion time) by up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 40%. The throughput under MBECN+ improves by 1.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim 2.4\times$</tex-math></inline-formula> than DCTCP and 1.26 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim 1.35\times$</tex-math></inline-formula> than ECN*.

DCCS: A dual congestion control signals based TCP for datacenter networks

As cloud computing advances, datacenter networks (DCNs) face unprecedented pressure due to the increasing prevalence of high bandwidth and low delay applications. Current reliable transmission protocols such as TCP harness packet loss, network delay, and Explicit Congestion Notification (ECN) for congestion control, aiming to maintain low latency and high throughput. We investigate the efficacy of ECN-based protocols against incast congestion in one-hop networks and delay-based protocols for maintaining low end-to-end delay in multi-hop networks, at the expense of throughput fairness. Thus, we introduce a novel TCP protocol, Dual Congestion Control Signals (DCCS), merging ECN and delay signals for a broader congestion control mechanism. DCCS effectively handles burst traffic, prevents packet loss, ensures low end-to-end delay, and resolves throughput unfairness by determining a precise minimum round-trip time (RTT) for each flow through a drain signal. Simulations on ns-3 confirm that DCCS surpasses ECN-based (i.e., DCTCP), delay-based (i.e., DC-Vegas and Swift), and hybrid-based protocols (i.e., EAR) in fat-tree topology under realistic workload, cutting down Flow Completion Time (FCT) by 3.8%–20.5%. This substantiates DCCS’s competency in providing high-quality service for DCNs.

DemePro: DEcouple packet Marking from Enqueuing for multiple services with PROactive congestion control

Most of current Data Center Network (DCN) protocols leverage Explicit Congestion Notification (ECN) for congestion control to maintain low latency and high throughput. However, the majority of them only consider single-queue scenario in each switch port, making their performance inferior in multiple-service multiple-queue scenario. To this end, we propose DemePro, a DCN scheme for multiple-service multiple-queue scenario. In face of congestion signal, DemePro also leverages ECN for congestion notification, while decouples packet marking from enqueuing, in order to ensure fairness among multiple services. We also question the effectiveness of the congestion signal derived from the single threshold for port buffer queue length, via a set of experiments. Then, DemePro utilizes multiple thresholds for proactive congestion control, and packets are encapsulated to carry congestion extent information, in order to notify TCP senders for precise and fine-grained congestion control. Experiments show that DemePro has better performance than MQ-ECN [1] which is currently the best DCN scheme for multiple-service multiple-queue scenario, and DemePro can also well guarantee the fairness.

DECC: Achieving Low Latency in Data Center Networks With Deep Reinforcement Learning

Data Center Networks (DCNs) suffer from synchronized bursts for segmentation and aggregation modes, leading to buffer overflows at switches and increasing network delay. To overcome this problem, some congestion control algorithms like DCTCP use Explicit Congestion Notification (ECN) to notify in-network congestion and reduce switch buffer occupancy. However, the traditional Additive Increase Multiplicative Decrease (AIMD) method causes high fluctuation of round-trip time (RTT) in DCNs. Some intelligent congestion control algorithms designed for Internet can achieve great flexibility, but are not applicable in DCNs for a lack of accurate congestion feedback. In this paper, we analyze the deficiencies of utilizing RTT as congestion signals and the applicability of learning algorithms in DCNs. Then, we propose DECC, a smart TCP congestion control algorithm for DCNs, which combines Deep Reinforcement Learning (DRL) with ECN to achieve high bandwidth utilization as well as low queuing delay. DECC fully utilizes precise in-network feedback and formulates several QoS requirements to a multi-objective function. Meanwhile, it decouples cwnd adjustment with DRL decision making to gradually learn the optimal congestion control policy in real-time. We evaluate the performance of DECC in various scenarios. Simulation results show that DECC can reduce the queue length at bottleneck switches by more than 50% compared to DCTCP, while maintaining high bandwidth utilization and reducing Flow Completion Time (FCTs) under burst traffic.

RLECN—A learning based dynamic threshold control of ECN

Explicit congestion notification (ECN) enables the network routers to mark packets instead of dropping them. When the queue size reaches a certain threshold, the queued packets are marked to indicate predicted congestion. However, an optimal value of the ECN threshold is not defined. A pre-decided value is chosen either by estimation or by hit and trial and therefore, it does not generalize well under a wide range of network scenarios. We propose a reinforcement learning (RL)-based ECN mechanism that utilizes software-defined networks (SDN) to address this problem. Our solution enables the routers to keep a dynamic ECN threshold according to the current network conditions. SDN provides the network visibility and reach to train the RL model and to dynamically adjust the ECN threshold. We show through experimental results that our proposed model outperforms the current state of the art.

A Receiver-Driven Transport Protocol With High Link Utilization Using Anti-ECN Marking in Data Center Networks

Existing reactive or proactive congestion control protocols are hard to simultaneously achieve ultra-low latency and high link utilization across all workloads ranging from delay-sensitive flows to bandwidth-hungry ones in datacenter networks. We present an Anti-ECN (Explicit Congestion Notification) Marking Receiver-driven Transport protocol called AMRT, which achieves both near-zero queueing delay and full link utilization by reasonably increasing sending rate in the case of under-utilization. Specifically, switches mark the ECN bit of data packets once detecting spare bandwidth. When receiving the anti-ECN marked packet, the receiver generates the corresponding marked grant to trigger more data packets. The testbed and simulation experiments show that AMRT effectively reduces the average flow completion time (AFCT) by up to 42% and improves the link utilization by up to 38% over the state-of-the-art receiver-driven transmission schemes.

TCP-Puerto-Londero: A New Approach for End-to-End Queue Length Control

This paper presents a new TCP protocol called TCPPuerto-Londero, which makes dynamic tuning in the congestion window (cwnd) by means of adaptive control theory aiming to keep stable and small the queue length in the bottleneck link located on the path from source to destination. This adaptive control loop has the relative delay in the forward path measured as an input and the cwnd value as an output. Thus, unlike classic TCP protocols, TCP-Puerto-Londero does not use Round Trip Time (RTT) information. Also, unlike classic Active Queue Management (AQM) strategies and Explicit Congestion Notification (ECN) based protocols, TCP-Puerto-Londero employs end-to-end queue management without the need for ECN resources. Moreover, TCP-Puerto-Londero also aims to attend to the new challenges of the Industrial Internet of Things (IIoT). Its performance has been tested in a Dumbbell network topology shared by TCP and UDP-like Networked Control Systems (NCS) flows. Therefore, TCP-Puerto-Londero performance has been compared with ECN-based protocols like DCTCP, E-DCTCP, TCP-Jersey, and ENCN. Furthermore, this paper employs an approach for modeling, analysis, simulation, and verification of the communication network and NCS employing UPPAAL simulation software tool, where all network constituents (transmitters, channels, routers, receivers, controllers, and plants) were modeled employing timed automata, simplifying a formal verification of the complete studied system. Simulations and statistical verification indicate that even though utilizing fewer resources (as it does not require the AQM/ECN information) TCP-Puerto-Londero overcomes TCP-Jersey, DCTCP, and EDCTCP with regard to throughput and fairness for TCP flows. Also, TCP-Puerto-Londero flows are capable to keep the queue length more stable and smaller than other protocols that were compared, and consequently, it reduces the impact in NCS-UDPlike flows sharing the same network, whose performance was measured employing the Integral Time Absolute Error (ITAE), which is a desirable feature for Industrial IoT.

DynaQ: Enabling Protocol-Independent Service Queue Isolation in Cloud Data Centers

Switches in cloud data centers support multiple service queues per port to provide differentiated network performance among different traffic classes. To isolate service queues, recent solutions leverage the power of Explicit Congestion Notification (ECN). However, this causes a fundamental dependency on ECN-based transport protocols, making it hard to use generic transport protocols. To this end, we design DynaQ, a protocol-independent multi-queue management solution that enables service queue isolation with generic transport protocols. The key idea of DynaQ is to adjust the packet dropping threshold of service queues dynamically. Specifically, DynaQ allows a service queue to occupy free buffer space but prevents the queue from hurting other active queues. Our solution requires only a few additional clock cycles to implement on hardware. To evaluate DynaQ comprehensively, we conduct a series of testbed experiments and large-scale simulations. Our evaluation results show that, compared to alternative schemes, DynaQ is the only solution that achieves work-conserving weighted fair sharing and low latency without protocol dependency.

Improvement of BBRv2 Congestion Control Algorithm Based on Flow-aware ECN

Google proposed a new congestion control algorithm (CCA) based on bottleneck bandwidth and round-trip propagation time (BBR), which is considered to open a new era of congestion control. BBR creates a network path model by measuring the available bottleneck bandwidth and the minimum round-trip time (RTT) to maximize delivery rate and minimize latency. The BBR v2 algorithm is a recently updated version by Google, which aims to improve some of the problems in the original BBR (BBRv1) algorithm, such as interprotocol fairness issues, RTT fairness issues, and excessive retransmissions. The BBRv2 evaluation results show that it can improve the coexistence with the loss_based algorithm and alleviate some of the shortcomings in BBRv1. However, when multiple BBRv2 flows enter the same link at different times, fair convergence cannot be achieved, and RTT fairness still exists. Based on these problems, we analyze the root cause and proposed an improved algorithm BBRv2+, which uses flow-aware explicit congestion notification (ECN) to quantify queue information and feedback on the accurate congestion degree. BBRv2+ algorithm can avoid blind window constraints and selectively mark packets so that different flows can converge to fairness. In the simulation experiment of Network Simulator 3 (NS3), the results show that the BBRv2+ algorithm can improve intraprotocol fairness and RTT fairness and ensure bandwidth utilization and interprotocol fairness.

Acknowledge-Based Non-Congestion Estimation: An Indirect Queue Management Approach for Concurrent TCP and UDP-Like Flows

This paper presents a new approach for indirect Active Queue Management (indirect AQM) technique called Acknowledge-based Non-Congestion Estimation (ANCE), which employs end-to-end queue management along a network instead to use Explicit Congestion Notification (ECN) bit or to drop packets in the queue. The ANCE performance was compared with Random Early Detection (RED), Control Delay (CoDel), Proportional Integral controller Enhanced (PIE), Explicit Non-Congestion Notification (ENCN), TCP-Jersey and E-DCTCP schemes in a daisychain and in a dumbbell cenario, with TCP flows and UDP-like Networked Control Systems (NCS) flow sharing the same network topology. On the other hand, this paper presents a method for modeling, simulation and verification of communication systems and NCS, using UPPAAL software tool, on which, all network components (channels, routers, transmitters, receivers, plants, and Controllers) were modeled using timed automata making easy a formal verification of the whole modeled system. Simulations and statistical verification show that despite using fewer resources (since ANCE does not need the ECN bit) ANCE presents a very close performance to ENCN overcoming Drop Tail, RED, CoDel, PIE and E-DCTCP in terms of Integral Time Absolute Error (ITAE) for NCS and fairness for TCP flows. ANCE also attains better performance than RED, PIE, TCP-Jersey and E-DCTCP in terms of throughput for TCP flows.

Harmonia: Explicit Congestion Notification and Credit-Reservation Transport Converged Congestion Control in Datacenters

Harmonia: Explicit Congestion Notification and Credit-Reservation Transport Converged Congestion Control in Datacenters

Active Queue Management in RED Considering Critical Point on Target Queue

The basic philosophy behind RED is to prevent congestion. When the average queue length exceeds the minimum threshold, packets are randomly dropped, or the explicit congestion notification bit is marked. Since network requirements differ significantly, it is not an optimal approach to establish RED parameters with constant value. There is a new algorithm we are proposing called Critical Point on Target Queue (AQM-RED-CPTQ), provide greater congestion management over the network while also preserving the value of RED. To overcome the problem in RED without changing queue weight parameter, we have proposed few models to control the congestion by introducing range parameter with probability and control mechanism which will belong between minimum and maximum threshold. The current queue size is controlled together with average queue size. A new range variable has been introduced to improve the performance of priority queue of existing RED based algorithm which improves the overall performance of networks. For each packet, minimum and maximum threshold has been updated and dropped with probability (Pa) for a special condition. Instead of multiplicative increase and decrease the maximum probability, the scheme uses additive-increase and multiplicative-decrease. Once the AVG queue length is close to the minimum threshold value, our approach automatically sets queue parameter according to queue conditions and handles queuing delay and improve throughput. The simulated results proof that our approaches are better than RED in terms of throughput, end to end delay, packet delivery ratio and goodput.

Choose a correct marking position: ECN should be freed from tail mark

Explicit Congestion Notification (ECN) has been widely employed in production datacenter networks (DCNs) to deliver high throughput and low latency communications. Despite being successful, the drawback of ECN-based transports is that they always mark the packets in the tail of the queue. We call this ECN marking scheme as TM-ECN (tail mark ECN). TM-ECN leads to long ECN feedback delay, significant queue oscillation, and poor short-flow friendliness. In this paper, we reveal these problems by leveraging extensive simulations to explore the intrinsic drawback of TM-ECN. Using the guidelines learned from the exploration, we design FM-ECN (front mark ECN) and RM-ECN (random mark ECN), two simple yet effective ECN marking schemes to mitigate the drawbacks. We theoretically analyze the TM-ECN, FM-ECN, and RM-ECN in the ECN feedback delay, queue stability, and short-flow friendliness. Finally, the conclusions from the analysis inspire us to propose a mixed mark ECN (MM-ECN) marking scheme. With the advantages of FM-ECN and RM-ECN, MM-ECN can maximize the effect of ECN. We further implement these ECN marking schemes in Linux kernel and ns-3 to evaluate them through testbed experiments and large-scale simulations. Our experimental results show that DCTCP with an appropriate ECN marking scheme achieves up to 55% higher concurrency tolerance of incast, and achieves up to 16% (25%) lower average (99th percentile) flow completion time (FCT) of short flows while delivering similar FCT for mid/large flows under production workloads.

Scalable Traffic Control Using Programmable Data Planes in a Space Information Network

The past several decades have witnessed wide deployment of space information networks (SINs). More than 8000 satellites have been launched into Earth's orbit recently. Meanwhile, the space communications requirements have grown by a factor of 10 in the last decade. The sharply increasing traffic volume puts tremendous pressure on the network traffic control in SINs. Current end-host-based schemes have to collect a massive amount of data (e.g., explicit congestion notification) to respond to one network event (e.g., topology change, network congestion), which is unacceptable in SINs due to the large end-to-end delay. Recently, with the emergence of programmable network devices, it has become possible to perform flexible control strategies as the traffic is flowing through the network (i.e., in-network traffic control). By implementing the control directly inside the network, the in-network schemes are more effective at scale and more responsive to network dynamics. Therefore, in this article, we design an in-network traffic control powered SIN architecture to enhance the network performance. In addition, we present two use cases, in-network load balance and in-network congestion control, to demonstrate the feasibility of our architecture. Extensive simulations are performed to evaluate our proposed schemes.

A Congestion-Aware Adaptive Streaming over ICN Combined with Explicit Congestion Notification for QoE Improvement

A Congestion-Aware Adaptive Streaming over ICN Combined with Explicit Congestion Notification for QoE Improvement

LossPass: Absorbing Microbursts by Packet Eviction for Data Center Networks

A bursty traffic pattern, called the microburst, is a key hurdle to achieve low latency for user-facing applications because it causes excessive packet losses in shallow buffered switches. Explicit Congestion Notification (ECN) can absorb microbursts by reserving buffer headroom, but the existence of headroom results in a fundamental trade-off between latency and throughput. To this end, we present LossPass, a buffer sharing mechanism that absorbs microbursts as many as possible while maintaining line-rate throughput. Specifically, LossPass evicts buffered large flow packets to make free buffer space on demand for arriving small flow packets. Our solution is inexpensive to implement on hardware. We implement a LossPass prototype and evaluate its performance through extensive testbed experiments and large-scale simulations. Our evaluation results show that LossPass reduces the FCT of small flows while maintaining line-rate throughput. For example, in testbed experiments, LossPass outperforms ECN by up to <inline-formula><tex-math notation="LaTeX">$3.20\times$</tex-math></inline-formula> in the 99th percentile FCT of small flows.

Mitigating TCP Protocol Misuse With Programmable Data Planes

This article proposes a new approach for detecting and mitigating the impact of misbehaving TCP end-hosts, specifically the Optimistic ACK attack, and Explicit Congestion Notification (ECN) abuse. In contrast to the state-of-the-art, we show that it is possible to mitigate such misbehavior leveraging emerging programmable data planes while not requiring any end-host or protocol modifications. A key challenge in doing so is to implement expressive, complex and stateful functions in the data plane within its restricted programming model. In this regard, we propose a security monitoring function that uses Extended Finite State Machine (EFSM) abstraction for monitoring stateful protocols in the data plane. We also design a mechanism for mapping a protocol’s EFSM to programmable data plane primitives. Our evaluation results demonstrate that our approach can fully or partially restore the throughput loss caused by misbehaving end-hosts that manipulate TCP congestion control through misinformation.

Low-Cost Datacenter Load Balancing With Multipath Transport and Top-of-Rack Switches

Load balancing in datacenter networks (DCNs) is an important and challenging task for datacenter managers. A number of sophisticated technologies have been proposed to improve load balancing performance in a complicated circumstance, i.e., with various traffic characteristics. Many approaches need a high cost to implement, such as changing switch hardware. The efficiency problem has not been well addressed. MPTCP was proposed as a low-cost approach to improve data transmission in DCNs, which uses subflows to balance workloads across multiple paths. However, current MPTCP is not satisfying, especially when there are rack-local flows or many-to-one short flows. In this article, we propose DCMPTCP to improve the efficacy of MPTCP. We gradually develop three mechanisms. First, DCMPTCP identifies rack-local traffic and eliminates unnecessary subflows to reduce the overhead. Second, DCMPTCP estimates flow length and establishes subflows in a smarter way. Third, DCMPTCP strengthens explicit congestion notification to improve the congestion control performance on inter-rack many-to-one short flows. We have implemented DCMPTCP in both the Linux kernel and ns-3 simulator. Our comprehensive testbed experiments and simulations show that DCMPTCP outperforms MPTCP in both 1 Gbps testbed, and 10 Gbps large-scale simulation network.