Mitigating congestion-based denial of service attacks with active queue management

A study of robust active queue management schemes for correlated traffic

A comparative simulation study of TCP/AQM systems for evaluating the potential of neuron-based AQM schemes

We present a virtual queue-based active queue management (AQM) scheme, called stabilized virtual buffer (SVB). The SVB scheme uses the packet arrival rate and queue length information to drop/mark packets probabilistically in a congested Internet router. System goodput, packet loss rate, average queue length, and stability of the queue are used to compare the proposed SVB scheme with prior AQM schemes (RED - random early detection; REM - random exponential marking; AVQ - adaptive virtual queue). Simulation results indicate that the SVB algorithm can provide better goodput and lower loss rate than the other three AQMs. The most striking feature of the proposed scheme is its robustness to workload fluctuations in maintaining a stable queue for different workload mixes (short and long flows) and parameter settings.

One of the challenges in the design of switches/routers is the efficient and fair use of the shared bottleneck bandwidth among different Internet flows. In particular, various active queue management (AQM) schemes have been developed to regulate transmission control protocol traffic in response to router congestion. In addition, in order to provide fair bandwidth sharing, these AQM must protect the well-behaved flows from the misbehaving flows. However, most of the existing AQM schemes cannot provide accurate fair bandwidth sharing while being scalable. The key to the scalability and fairness of the AQM schemes is the accurate estimation of certain network resources without keeping too much state information. We propose a novel technique to estimate two network resource parameters: the number of flows in the buffer and the data source rate of a flow by using a capture-recapture (CR) model. The CR model depends on simply the random capturing/recapturing of the incoming packets, and as a result, it provides a good approximation tool with low time/space complexity. These network resource parameters are then used to provide fair bandwidth sharing among the Internet flows. Our experiments and analysis will demonstrate that this new technique outperforms the existing mechanisms and closely approximates the ideal case, where full state information is needed.

Several active queue management (AQM) schemes have been proposed to address the bufferbloat issue, a long network delay and performance degradation issue caused by excess buffering of packets in network nodes. The solutions proposed up to now, however, satisfy only a subset of the key network performance metrics, i.e., end-to-end throughput, throughput fairness, and delay, in 802.11s-based multi-radio multi-channel wireless mesh networks (MRMC WMN). In this letter, we propose FD-AQM, a fairness-aware delay-controlled AQM to satisfy all three key network performance metrics simultaneously in the 802.11s-based MRMC WMN. The proposed FD-AQM achieves the goal by adopting a per-queue drop interval which is recalibrated according to the average round-trip time of flows, and a drop decision mechanism which considers average throughput as well as target delay. Through simulations, we show that FD-AQM is superior to other AQM schemes in terms of throughput and fairness.

Congestion control in TCP/AQM networks is expected to perform well for a wide-range of conditions, but recent advances in modeling and analysis indicate that present AQM (active queue management) schemes need an extra dose of adaptability to cope. The paper answers the call and proposes a self-tuning structure wherein AQM parameters are automatically tuned in response to on-line estimation of link capacity and traffic load. This approach is applicable to any AQM scheme that is parameterizable in terms of link capacity and TCP load. We describe this self-tuning structure, illustrate its application to PI (proportional-integral) and RED (random early detection) AQMs, provide stability analysis, and conduct ns simulations to compare with both fixed AQM schemes and the recently proposed adaptive RED.

This paper proposes a partial state Active Queue Management (AQM) technique, called CHOKe-FS, which addresses the problem of fair-bandwidth allocation among different flows sharing a common bottleneck link. CHOKe-FS is a packet-dropping mechanism for classifying and restricting unresponsive or misbehaving flows during congestion. It utilizes a direct bitmap technique, which estimates the number of active flows in the buffer to compute the fair-share. It inherits the matched drop mechanism of CHOKe and the buffer management technique of RED. The proposed technique differs from CHOKe in three ways: (i) it divides the queue into multiple regions, (ii) it dynamically adjusts the drawing factor depending on the average queue occupancy, by drawing multiple packets from the buffer, and (iii) it estimates the number of active flows in a queue for fair share estimation to drop the packet from flows exceeding the fair bandwidth share. The performance of CHOKe-FS is analyzed under various scenarios by comparing it with the well-known, AQM schemes such as RED, CHOKe and CHOKeR. The results demonstrate that the proposed algorithm not only protects the responsive flows from unresponsive flows, but it also works reasonably well for providing intra-protocol fairness.

Loss differentiation is recommended as a service differentiation provided by an assured forwarding (AF) per-fop behavior (PHB) in differentiated service (DiffServ) architecture. An active queue management (AQM) technique is addressed as a suitable alternative to realize the service differentiation because the AF PHB should attempt to minimize long-term congestion while permitting short-term congestion in order to accommodate traffic bursts. In order to realize the AF PHB using an AQM scheme, it is desirable that the AQM scheme has DVO properties of sheltering and load tolerance in order to protect low drop precedence traffic and to prevent starvation of high drop precedence traffic. In this paper, we introduce another desirable property of assured drop probability. We modify an existing AQM algorithm for the property so that it assures a target drop probability in a properly provisioned network. Other properties of sheltering and load tolerance still hold for the modified AQM scheme. We evaluate it with other comparable schemes, i.e., WRED and RIO through simulation.

In recent years, it has become an active research direction to develop adaptive and robust active queue management (AQM) scheme for congestion control of complex time-varying network. A novel adaptive AQM scheme based on Neuron Reinforcement Learning (NRL) is presented in this paper. This scheme uses queue length and link rate as congestion notification to determine an appropriate drop/mark probability, and the parameters of neuron can be adjusted online according to the time-varying network environment so that the stability of queue dynamics and robustness for fluctuation of TCP loads are guaranteed. This scheme is easy to implement with simple structure, and it is independent of the model of plant to be controlled. Simulation results show that this proposed algorithm is especially suitable for solving the complex network congestion control problem, and also has better stability and robustness.

Recognizing that Internet congestion control is a complex nonlinear system, we propose here to use an intelligent controller to improve its stability and performance. In particular, we propose here a new, powerful, easy-to-configure and robust active queue management (AQM) scheme called adaptive neuron AQM (AN-AQM). We present extensive simulation results for AN-AQM, over a wide range of network conditions and scenarios, that demonstrate its attributes. We demonstrate its robustness in various realistic environments involving bursty HTTP connections and non-responsive UDP connections. Comparison with other AQM schemes has demonstrated the superiority of AN-AQM over well-known AQM schemes in achieving faster convergence to queue length target, and smaller queue length jitter.KeywordsCongestion controlActive queue managementNeuronAQM

A novel active queue management scheme is proposed to employ in the routers that deal with both the best-effort traffic flows and multimedia traffic flows. Most of the available active queue management schemes consider only TCP flows, which results in unexpected congestion when dealing with multimedia flows. The queue size and packet receiving rate are the common parameters to calculate the marking probability in the queue. The use of round trip time (RTT) in the process of marking probability calculation has advantages over other approaches. We discuss the applicability of our approach when the framework in our earlier paper is used for rate adaptation in multimedia flows. With the use of RTT in packet marking probability calculations, we can assure rate reduction in both best-effort and multimedia flows before a queue gets exhausted. The most influential feature of the proposed approach is that it can work with the existing systems. The soundness of the proposed approach is inferred from the simulation results.

Active queue management (AQM) at routers can effectively control the queue length while keeping high throughput of routers. In this paper, a new AQM algorithm called Hang algorithm based on a modified Smith predictor is proposed. Theoretic analysis and extensive simulation shows that the developed control scheme is effective in dealing with transmission delay and other uncertainties. Comparison with traditional PI control and PI-Smith Predictor control is also conducted via NS2 simulation, and the results confirm the effectiveness of the proposed method in accounting for load disturbance and delay variation.

Internet of Things (IoT) has pervaded most aspects of our life through the Fourth Industrial Revolution. It is expected that a typical family home could contain several hundreds of smart devices by 2022. Current network architecture has been moving to fog/edge architecture to have the capacity for IoT. However, in order to deal with the enormous amount of traffic generated by those devices and reduce queuing delay, novel self-learning network management algorithms are required on fog/edge nodes. For efficient network management, Active Queue Management (AQM) has been proposed which is the intelligent queuing discipline. In this paper, we propose a new AQM based on Deep Reinforcement Learning (DRL) to handle the latency as well as the trade-off between queuing delay and throughput. We choose Deep Q-Network (DQN) as a baseline of our scheme, and compare our approach with various AQM schemes by deploying them on the interface of fog/edge node in IoT infrastructure. We simulate the AQM schemes on the different bandwidth and round trip time (RTT) settings, and in the empirical results, our approach outperforms other AQM schemes in terms of delay and jitter maintaining above-average throughput and verifies that DRL applied AQM is an efficient network manager for congestion.

This paper is concerned with the design of robust active queue management (AQM) controllers for a class of TCP networks. In TCP/IP networks, the packet-dropping probability function is limited between 0 and 1. Therefore, a TCP network with AQM controller was modeled as a rate-based non-linear system with a saturated input. The nonlinear output feedback AQM controller is proposed for achieving the desired queue size and guaranteeing asymptotic stability of the operating point. The performance and effectiveness of the proposed control laws are validated for serial network scenarios through numerical simulations by Network Simulator-2 (NS-2). Simulation results confirm that the proposed scheme outperforms other AQM schemes.

In this paper, a novel active queue management scheme is proposed to control the congestion in the networks using TCP/IP protocol for transport layer. Active queue management (AQM) is a complement protocol to TCP/IP which provides feedback to the TCP sources in order to perform close loop congestion control. We use the nonlinear model of TCP/AQM system and build our controller on this realistic model. The control parameter is the drop probability of AQM technique. Our controller is designed based on integral backstepping technique. The main advantage of our AQM technique compared to other is the robustness of the algorithm with respect to changes in the system parameters such as the number of sources of the round trip time. Next, we provide conditions which guarantee that the control input which is the packet dropping probability falls between 0 and 1. Finally, we evaluate the performance of our proposed scheme for various network conditions and parameters. Compared to the other AQM controllers, the simulation results obtained with Simulink, demonstrate the effectiveness of the proposed method.