Analyzing the Receiver Window Modification Scheme of TCP Queues

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.

Random early detection (RED) is widely deployed as a congestion control mechanism in the routers. However, RED is often limited by the difficulty of tuning its parameters under different congestion levels. Adaptive RED (ARED) addresses this problem by adaptively adjusting the aggressiveness of RED settings to keep the average queue size between minimum threshold (minth) and maximum threshold (maxth ). We have observed that the additive increase multiplicative decrease (AIMD) approach used in ARED can always maintain high link utilization but not effective enough in preventing high packet loss rates under heavy congestion. In this paper, we propose an adaptive version of RED which applies non-linear power function instead of linear AIMD approach to achieve better link utilization as well as minimizing the packet loss rates under various traffic load conditions

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.

In this paper we propose a fuzzy logic implementation of random early detection (RED) mechanism. The main objective of the proposed fuzzy controller is to reduce the loss probability of the RED mechanism without any change in channel utilization. Using fuzzy logic capabilities, we try to dynamically tune the loss probability of RED gateway. To achieve this goal, we use a two-input-single-output fuzzy controller. The inputs of fuzzy controllers are: 1) the error signal e1 which represents the difference between average queue size and a target point. 2) The error signal e2, which is calculated as the difference between the estimated value of incoming data rate and the target link capacity. The output of fuzzy controller is used to dynamically tune the max p parameter of RED gateway. All simulation results show that the proposed fuzzy mechanism has better performance than traditional RED and adaptive RED (ARED) mechanisms.

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 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.

A study of robust active queue management schemes for correlated traffic

In this paper we compare the three Active Queue Management (AQMs) Adaptive Random Early Detection (ARED), Controlled Delay (CoDel) and Proportional Integral controller Enhanced (PIE) in static as well as dynamic scenarios. We find significant issues when these algorithms are used for big Round Trip Times (RTTs) as well as a significant utilization decrease when used for high bandwidth links. When used for low and medium sized links, CoDel, PIE and ARED are suitable alike, but for corner scenarios clear recommendations can be given.

Low-latency, interactive media applications (e.g., video conferencing) present a unique set of challenges for congestion control. Unlike TCP, the transport mechanism for interactive media needs to adapt fast to abrupt changes in available bandwidth, accommodate sluggish responses and output rate fluctuations of a live video encoder, and avoid high queuing delay over the network. An ideal scheme should also make effective use of all types of congestion signals from the network, including packet losses, queuing delay, and explicit congestion notification (ECN) markings. This paper presents a unified approach for congestion control of interactive video: network-assisted dynamic adaptation (NADA). In NADA, the sender regulates its sending rate based on a composite congestion signal calculated and reported by the receiver, which combines both implicit (e.g., loss and delay) and explicit (e.g., ECN marking) congestion indications from the network. Via a consistent set of sender adaptation rules, the scheme can reap the full benefits of proactive, explicit congestion notifications supported by advanced queue management schemes. It remains equally responsive in the absence of such notifications. Extensive simulation studies show that NADA interact well with a wide variety of queue management schemes: conventional drop-tail, random early detection (RED), recently proposed CoDel (controlled delay) and PIE (Proportional Integral controller Enhanced), as well as a token-bucket-based random marking scheme based on Pre-Congestion Notification (PCN). Furthermore, NADA reacts fast to changes over the network, allows for weighted bandwidth sharing among multiple competing video streams, and sustains a substantial share of bottleneck bandwidth when coexisting with TCP.

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.

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.

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.

This paper presents a comprehensive study about the impact of active queue management (AQM) on Voice over Internet Protocol (VoIP) quality of service. One of the most representative AQM scheme is analyzed through extensive simulation and its effect on the perceived quality of voice calls is evaluated. Different network scenarios, changing network loads and different scheme control parameters are investigated. Network impairments are related to user perception by means of well known algorithmic models, expressing user satisfaction in the MOS scale. The main results obtained show that the use of active queue management schemes like adaptive random early detection (ARED) significantly improve the perceived quality of voice calls.

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.

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.