Stabilized virtual buffer (SVB) - an active queue management scheme for Internet quality-of-service

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

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.

Denial of service (DoS) attacks are currently one of the biggest risks any organization connected to the Internet can face. Hence, the congestion handling techniques at its edge router(s), such as active queue management (AQM) schemes must consider possibilities of such attacks. Ideally, an AQM scheme should (a) ensure that each network flow gets its fair share of bandwidth, and (b) identify attack flows so that corrective actions (e.g. drop flooding traffic) can be explicitly taken against them to further mitigate the DoS attacks. This paper presents a proof-of-concept work on devising such an AQM scheme, which we name Deterministic Fair Sharing (DFS). Most of the existing AQM schemes do not achieve the above goals or have a significant room for improvement. DFS uses the concept of weighted fair share (wfs) which allows it to dynamically self-adjust the router buffer usage based on the current level of congestion, while assuring fairness among flows and aiding in identifying the malicious ones. We demonstrate the performance of DFS via extensive simulation and compare against other existing AQM techniques.

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.

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.

Explicit congestion notification (ECN) and active queue management (AQM) Schemes such as random early detection (RED), adaptive random early detection (ARED) and BLUE queues have been proposed for TCP/IP networks to compensate network congestion. However, using ECN requires that ECN be supported by both TCP senders and receivers. This paper presents a novel AQM modification called receiver- window modification (RWM). RWM can be used together with RED, ARED and BLUE queues, to provide congestion avoidance in packet switched networks at ingress and gateway routers. RWM does not require modification to all end system TCP/IP stacks but can be solely implemented in routers. Our RWM scheme helps in reducing the average queue sizes of RED, ARED, BLUE and even ARED-ECN, BLUE-ECN and RED- ECN queues. By reducing the average queue sizes, RWM queues reduce the queuing delay resulting in significant improvements in one-way end-to-end packet delays and dropped packets. It is also shown that the performance of RED- ECN, ARED-ECN and BLUE-ECN queues is heavily dependent on the queues of the downstream routers. RWM modified queues in ingress or gateway routers are not influenced by the number and state of the downstream router as they will piggyback congestion information to the source in the next available acknowledgement packet. We carry out extensive ns2 simulations to show our results and to support our claims.

The explosive increase in the volume and variety of Internet traffic has placed a growing emphasis on congestion control and fairness in Internet routers. Approaches to the problem of congestion, such as active queue management schemes like Random Early Detection (RED) use congestion avoidance techniques and are successful with TCP flows. Approaches to the problem of fairness, such as Fair Random Early Drop (FRED), keep per-flow state and punish misbehaved, non-TCP flows. Unfortunately, these punishment mechanisms also result in a significant performance drop for multimedia flows that use flow control. We extend Class-Based Threshold (CRT) [12], and propose a new active queue management mechanism as an extension to RED called Dynamic Class-Based Threshold (D- CBT) to improve multimedia performance on the Internet. Also, as an effort to reduce multimedia jitter, we propose a lightweight packet scheduling called Cut-In Packet Scheduling (ChIPS) as an alternative to FIFO packet scheduling. The performance of our proposed mechanisms is measured, analyzed and compared with other mechanisms (RED and CBT) in terms of throughput, fairness and multimedia jitter through simulation using NS. The study shows that D-CBT improves fairness among different classes of flows and ChIPS improves multimedia jitter without degrading fairness.

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

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.

Last decade seen an astronomical growth in the development of the multi-hop wireless technologies such as mobile ad-hoc network, vehicular ad-hoc network and cognitive radio network, the network congestion poses a serious threat in full scale deployment of wireless multi-hop technologies towards internet architecture. An active queue management (AQM) is a proactive, effective congestion control mechanism deployed at the intermediate gateway. Random Early Detection (RED) is the earliest and most widely used Active Queue Management (AQM) mechanism in backbone gateways, which in turn reduce global synchronization and bias against burst traffic. The traditional RED and its variants such GRED, AGRED, ARED, NL-RED, REM, RIO, etc. has more parameters to be tuned each time for the arrival of the packet. The tuning of more parameters leads to computation load, so it is unsuitable for low memory less devices such as laptops, handset, etc. In this paper, a micro level analysis was made on standard AQM's under multi-hop wireless environment. The results were analyzed based on the metrics such as average queue length, packet loss, queuing delay, end to end delay and throughput. This work forms a base work in designing a new queuing algorithm over heavily tailed multi-hop networks.

Random early detection (RED) is an active queue management (AQM) mechanism for routers on the Internet. In this paper, performance of RED and adaptive RED is compared from the viewpoint of nonlinear dynamics. In particular, we reveal the relationship between the performance of the network and its nonlinear dynamical behavior. We measure the maximal Lyapunov exponent and Hurst parameter of the average queue length of RED and adaptive RED, as well as the throughput and packet loss rate of the aggregate traffic on the bottleneck link. Our simulation scenarios include FTP flows and Web flows, one-way and two-way traffic. In most situations, adaptive RED has smaller maximal Lyapunov exponents, lower Hurst parameters, higher throughput and lower packet loss rate than that of RED. This confirms that adaptive RED has better performance than RED.

The bifurcation and chaotic behavior of TCP may cause heavy oscillation of average queue length at routers and induce network instability. In this context, congestion control in the Internet is a challenging problem of key importance. This work presents a new discrete dynamical model of Random Early Detection (RED) using beta distribution for controlling bifurcations and chaos in the internet congestion control. The numerical analysis and the simulation experiments show that this new Active Queue Management (AQM) model can obtain the stable average queue length to the desired fix point. The model programmed with Python and Mathematica incorporates new parameters (α, β) that make it possible to stabilize oscillations of averaged router queue length and to be close to stationary state.

In this paper, we propose a Takagi-Sugeno (T-S) fuzzy controller for active queue management (AQM) with explicit congestion notification (ECN) to improve the performance of Internet communications. We use the queue length and incoming flow rate to measure the degree of congestion effect on Internet to regulate the queue length at a specified level. Linear matrix inequality (LMI) techniques are employed to obtain the feedback gains. To proceed farther into the verification of proposed scheme, we make use of the network simulation (NS-2) in our experiment. Comparing with other AQM schemes, the experiment results show that the proposed AQM has better performance in terms of packet loss, throughput and the fluctuation of queue length.