Bifurcation analysis for the Internet congestion

Active Queue Management (AQM) is an active research area in the Internet community. Random Early Detection (RED) is a typical AQM algorithm, but it is known that it is difficult to configure its parameters and its average queue length is closely related to the load level. This paper proposes an effective fuzzy congestion control algorithm based on fuzzy logic which uses the predominance of fuzzy logic to deal with uncertain events. The main advantage of this new congestion control algorithm is that it discards the packet dropping mechanism of RED, and calculates packet loss according to a preconfigured fuzzy logic by using the queue length and the buffer usage ratio. Theoretical analysis and Network Simulator (NS) simulation results show that the proposed algorithm achieves more throughput and more stable queue length than traditional schemes. It really improves a router’s ability in network congestion control in IP network.

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.

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.

This paper proposes a router congestion control mechanism called PRED (Prediction-enabled RED), a more adaptive and proactive version of RED (Random Early Detection). In essence, PRED predicts its queue length for an early detection of possible congestion alerts in the near future and operates adaptively to the predicted changes in traffic patterns. Typically, PRED does this by first making prediction about average queue length and then using the predicted average queue length to adjust three classic RED parameters max th, min th, and max p. The incoming packets after the adjustment are now being dropped with the new probability defined by updated parameters. Due to its adaptability and proactive reaction to network traffic changes, PRED can be considered as a novel solution to dynamically configure RED. Extensive simulation results from NS-2 simulator are presented to verify the performance and characteristics of PRED.

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.

Congestion control in Cluster Wireless Sensor Networks (CWSNs) has drawn widespread attention and research interests. The increasing number of nodes and scale of networks cause more complex congestion control and management. Active Queue Management (AQM) is one of the major congestion control approaches in CWSNs, and Random Early Detection (RED) algorithm is commonly used to achieve high utilization in AQM. However, traditional RED algorithm depends exclusively on source-side control, which is insufficient to maintain efficiency and state stability. Specifically, when congestion occurs, deficiency of feedback will hinder the instability of the system. In this paper, we adopt the Additive-Increase Multiplicative-Decrease (AIMD) adjustment scheme and propose an improved RED algorithm by using neighbor feedback and scheduling scheme. The congestion control model is presented, which is a linear system with a non-linear feedback, and modeled by Lur’e type system. In the context of delayed Lur’e dynamical network, we adopt the concept of cluster synchronization and show that the congestion controlled system is able to achieve cluster synchronization. Sufficient conditions are derived by applying Lyapunov-Krasovskii functionals. Numerical examples are investigated to validate the effectiveness of the congestion control algorithm and the stability of the network.

AQM (Active Queue Management) is an effective scheme to enhance congestion control. Most of the mobile networks today use queue management to manage and improve network performance. In this paper, we deploy AQM mechanism in Mobile Ad Hoc networks to manage the packets queue in the network nodes. By modeling a node with multi-receiver in Ad Hoc networks, we develop an analytical model for a multi-server and finite capacity queuing system with AQM mechanisms under multi-classes of traffic. We consider multiple classes of traffic modeled by Poisson arrival and exponentially service processes. It deduced the performance metrics including the average queue length, system throughput, probability of drop packet and the average waiting time. Then, it illustrated the relationship between above metrics and the number of servers. Finally, by demonstrating the effective number of servers, it illuminate that we should deploy appropriate number of servers for a certain condition.

The purpose of this research is to design and evaluate a new queue management algorithm effectively controlling the heavy-tailed traffics. Among active queue managements (AQM), Random Early Detection (RED), has been widely used to improve the TCP throughput. The heavy-tailed traffic, however, generates the large fluctuation on the queue causing the degradation of fairness in terms of jitter performance. In this paper, we proposed the Stable time RED (STRED) to improve the jitter and the fairness of jitter performance. STRED enables to stabilize the calculation timing for average queue length leading to suppress the queue fluctuations. We also implemented the STRED mechanism on the ns-2 simulator. In addition, we propose the pseudo model network consisting of multiple input / output buffers. The experiments of different pattern of AQM algorithms were conducted. As the results of simulation, we extracted the following features. Firstly, STRED could sustain the jitter values and large fluctuations of each file size and Pareto distribution. Secondly, STRED at the first incoming buffer made lower and smoother jitter values than the RED method. Thirdly, STRED could improve the fairness of jitter for all file sizes, and especially for heavy-tailed traffics. Finally, STRED creates low jitters and the high fairness in term of jitter meaning that STRED can enhance the quality of mutual communication applications such as Internet telephony.

When the number of packets increases beyond the network capacity leads to congestion in the computer network. Congestion effects the QOS parameters severely by decreasing throughput and increasing packet loss and delay, thus degrading the overall network performance. Active queue management algorithm is router based mechanism for early detection of congestion in the computer network. The basic idea of AQM is to sense and detect congestion in advance and to inform the sender to reduce its sending rate there by reducing the number of packets sent in the network to control the congestion in the CN. AQM algorithm is congestion control algorithm which increases throughput, link utilization and decreases the delay and packet loss. RED (Random Early Detection) provides the mechanism for congestion avoidance. The technique has several drawbacks and one among them is inability to deal with busty traffic. The major drawback of Blue algorithm is that it detect the congestion by analyzing and observing the queue length, queue length may little information about the severity of the congestion and detection of number of competing connections. The proposed algorithm called AdvancedBlue which is the modification of Blue AQM algorithm uses idle link, packet loss to detect congestion decouples the congestion measure with performance measure and stabilizes the performance measure against the target independently of number of users. AdvancedBlue AQM is simulated in wireless network using NS2 simulator. Wireless network is quite different from the wired network. The nodes have no wired links to other nodes and base station. So any barrier that is between the sender and the receiver could influence the transmission power and bring packet loss. The proposed AQM showed a better performance in wired network and was more complicated in wireless network. The proposed algorithm when compared with existing DropTail algorithm in wireless network showed better performance with respect to throughput and delay but had more packet loss. Keywords: AQM, AdvancedBlue, Blue, CN, DropTail, NS2, QOS, RED Cite this Article: Uma R. Pujeri. Advanced AQM Blue Algorithm in Wireless Network. International Journal of Wireless Network Security. 2020; 6(1): 16–24p.

A framework to determine bounds of maximum loss rate parameter of RED queue for next generation routers

Queue length is an important index of the efficiency of urban transport system. The traditional approaches seem insufficient for the estimation of the queue length when the traffic state fluctuates greatly. In this paper, the problem is solved by introducing the Cell Transmission Model, a macroscopic traffic flow, to describe the vehicles aggregation and discharging process at a signalized intersection. To apply the model to urban traffic appropriately, some of its rules were improved accordingly. Besides, we can estimate the density of each cell of the road in a short time interval. We, first, identify the cell, where the tail of the queue is located. Then, we calculate the exact location of the rear of the queue. The models are evaluated by comparing the estimated maximum queue length and average queue length with the results of simulation calibrated by field data and testing of queue tail trajectories. The results show that the proposed model can estimate the maximum and average queue length, as well as the real-time queue length with satisfactory accuracy.

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

Random Early Detection (RED) is one of the most commonly used Active Queue Management (AQM) algorithms that is recommended by IETF for deployment in the network. Although RED provides low average queuing delay and high throughput at the same time, but effectiveness of RED is highly sensitive to the RED parameters setting. As network condition varies largely, setting RED's parameters with fixed values is not an efficient solution. We propose a new method to dynamically tuning RED's parameters. For this purpose, we compute the rate of which the queue is occupied and consider it as a congestion metric that will be forecasted when the queue is overloaded. This meter is used to dynamically setting RED parameters. The simulation results show the effectiveness of the proposed method. According to the results, we achieve a significantly higher utilization and less packet loss comparing to original RED algorithm in dynamic conditions of the network.

Congestion is a key topic in computer networks that has been studied extensively by scholars due to its direct impact on a network’s performance. One of the extensively investigated congestion control techniques is random early detection (RED). To sustain RED’s performance to obtain the desired results, scholars usually tune the input parameters, especially the maximum packet dropping probability, into specific value(s). Unfortunately, setting up this parameter into these values leads to good, yet biased, performance results. In this paper, the RED-Exponential Technique (RED_E) is proposed to deal with this issue by dropping arriving packets in an exponential manner without utilizing the maximum packet dropping probability. Simulation tests aiming to contrast E_RED with other Active Queue Management (AQM) methods were conducted using different evaluation performance metrics including mean queue length (mql), throughput (T), average queuing delay (D), overflow packet loss probability (PL), and packet dropping probability (DP). The reported results showed that E_RED offered a marginally higher satisfactory performance with reference to mql and D than that found in common AQM methods in cases of heavy congestion. Moreover, RED_E compares well with the considered AQM methods with reference to the above evaluation performance measures using minimum threshold position (min threshold) at a router buffer.

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.