An improvement algorithm based on RED and its performance analysis

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.

In Internet gateways with active queue management, effective algorithms are often used to enhance the traffic flows through the gateway. The random early detection (RED) algorithm is a widely used algorithm for controlling the transmission control protocol (TCP) flows. Specifically, the RED algorithm has deliberately introduced randomness into the explicit congestion notification (ECN) marking mechanism in order to enhance the fairness and to avoid synchronization of TCP flows through the gateway. As randomness plays a key role in providing better performance for RED gateways than the DropTail gateways, knowledge of the effect of the random mechanism is important to the understanding the behavior of the system. The fluid flow model (FFM), ignoring the randomness of ECN markings, has provided a fast alternative to the slow ns-2 network simulation tool in studying the TCP flow in the RED gateway. In this brief, the random ECN marking mechanism in RED gateways has been studied. The randomness of the RED ECN marking algorithm is implemented into the FFM. Verified using ns-2 simulations, the proposed model shows better dynamical performance than FFM in both the single-bottleneck dumbbell network and in networks with interactive bottlenecks.

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.

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.

Schemes described in the literature on network congestion management are in general based on queue management. It is widely accepted that Poisson model is not sufficient to characterize the traffic in current Internet. In this paper, we present an alternate RED (Random Early Detection) algorithm for traffic congestion management in IP networks having self-similar (SS) input. We first discuss the basic scheme of Normal and Gentle RED as proposed by Floyd et.al., for the Poisson input model, and then explain piecewise RED, an extension of Gentle RED and Exponential RED - a new AQM proposed by us. We then explain the modification to these algorithms that we propose for the self-similar IP traffic input. Our modification takes into consideration probability values corresponding to the average queue lengths for computing the marking/dropping probability. Verification of the algorithms proposed viz., Normal SS RED and Exponential SS RED, vis-a-vis that of Floyd has been done using simulated self-similar traffic. Results of the verification have been discussed in the paper.

Random Early Detection(RED) algorithm of Active Queue Management(AQM) is a well-known link congestion control algorithm. Based on analyzing the linear RED algorithm this paper proposes a nonlinear feedback method of composite curves which used to notify end system congestion state feedback mechanism. When the new mechanism is applied to the queue management process of the router buffer, the result of the simulations indicate the effectiveness of the new algorithm. Therefore more effective congestion control target is achieved.

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.

Low‐rate denial of service (LDoS) attacks exploit the congestion control mechanism to degrade the network quality of service. As a classic active queue management algorithm, random early detection (RED) algorithm is widely used to avoid network congestion. However, RED is vulnerable to LDoS attacks. LDoS attacks with well‐configured attack parameters force RED queue to fluctuate severely, thereby throttling transmission control protocol (TCP) senders’ sending rate. A feedback control model is proposed to describe the process of the congestion control, by which the congestion window and queue behaviours are analysed combined. After that, a two‐dimensional queue distribution model composed of the instantaneous queue and the average queue is designed to extract the attack feature. Moreover then, a combination of a simple distance‐based approach and an adaptive threshold algorithm is proposed to detect every LDoS attack burst. Test results of network simulator (NS)‐2 simulation and test‐bed experiments indicate that the proposed detection strategy can almost completely detect LDoS attack bursts and is especially robust to legitimate short bursts.

Random Early Detection(RED) is a network congestion control algorithm which calculates the packet-lossratio according to current length of average queue. This paper describes an improved RED algorithm: Firstly, predicts network flows with RBF neural Network in order to forecast queue length much earlier, and then, fits the Packet-loss-ratio of RED algorithm according to some special points using curve fitting method, therefore, controls nonlinear network congestion. From our simulation, it can be concluded that the algorithm avoids congestion more efficiently.

Deploying the active queue management (AQM) algorithm on a router is an effective way to avoid packet loss caused by congestion. In an information-centric network (ICN), routers not only play a role of packets forwarding but are also content service providers. Congestion in ICN routers can be further summarized as the competition between the external forwarding traffic and the internal cache response traffic for limited bandwidth resources. This indicates that the traditional AQM needs to be redesigned to adapt to ICN. In this paper, we first demonstrated mathematically that allocating more bandwidth for the upstream forwarding flow could improve the quality of service (QoS) of the whole network. Secondly, we propose a novel AQM algorithm, YELLOW, which predicts the bandwidth competition event and adjusts the input rate of request and the marking probability adaptively. Afterwards, we model YELLOW through the totally asymmetric simple exclusion process (TASEP) and deduce the approximate solution of the existence condition for each stationary phase. Finally, we evaluated the performance of YELLOW by NS-3 simulator, and verified the accuracy of modeling results by Monte Carlo. The simulation results showed that the queue of YELLOW could converge to the expected value, and the significant gains of the router with low packet loss rate, robustness and high throughput.

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 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.

This paper presents a new traffic management algorithm able to meet the requirements of high speed networks where the speed of ingress traffic can exceeds 10 Gbps. Our newly algorithm, called HRED (high speed RED) is based on the well known algorithm RED (random early detection) algorithm with several enhancements. Unlike other existing enhancements for RED which are found in the literature, our proposed changes leads to an algorithm which outperforms the existing ones in terms of packet drops. The authors present in this paper a series of simulation results demonstrating this fact. Also, the authors developed an efficient heuristic used in the hardware implementation of the algorithm and proved that this heuristic diminishes the response time of the algorithm. The reason for which we claim it's suitability for high speed networks.

With growing internet using demands, the need of services at various scales of Quality of Service (QoS) has made appropriate classification of these types of services mandatory. Therefore, the Differentiated Services Architecture has been proposed for service differentiation at various scales of services. DiffServ network is based on traffic classification mechanism. To measure the measurement of network performance of queue mechanism at router is a very crucial and important task. A number of queue algorithms have been proposed to reduce the routing problems in DiffServ network where priority of packets is a major concern for evaluating the network performance. This paper focuses on queue mechanism at edge router which has to select the higher priority packets enter first into the network. The aim of this work is to minimize the number of drop packets at the edge router. The paper considers performance (in terms of no. of packet drop, average end to end delay) comparison of Random Early Detection algorithm (RED) and Virtual Output Queue (VOQ) algorithm in different network conditions using Network Simulator-2.

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.