Random Early Detection Research Articles

QRED: an enhancement approach for congestion control in network communications

This Nowadays, along with the rapid development of Internet technology, the user’s network functionality and need for internet performance are also growing very fast. Therefore network congestion being increasingly serious and congestion control is becoming a more important issue. To detect and control congestion many queue management techniques are accessible; the extremely fundamental of them is Random Early Detection (RED). In order to enhance network performance, Quadratic Random Early Detection (QRED) is proposed. This paper gives comparative analysis between RED and QRED and shows that proposed method has improved network performance in the congested network.

Digital Bifurcation Analysis of Internet Congestion Control Protocols

Digital bifurcation analysis is a new algorithmic method for exploring how the behavior of a parameter-dependent discrete system varies with a change in its parameters and, in particular, for the identification of bifurcation points where such variation becomes dramatic. We have developed the method in an analogy with the traditional bifurcation theory and have successfully applied it to models taken from systems biology. In this paper, we report on the application of the digital bifurcation analysis for analyzing the stability of internet congestion control protocols by inspecting their attractor bifurcations. In contrast to the analytical methods, our approach allows fully automated analysis. We compared the robustness of the basic Random Early Drop (RED) approach with four substantially different extensions, namely gentle, adaptive, gradient descent, and integral feedback RED. The basic RED protocol is well known to exhibit unstable behavior when parameters are varied. In the case of adaptive and gradient descent RED protocol, the analysis showed significant improvements in stability, whereas in the results for gentle and integral feedback RED protocols the improvement was negligible. We performed a series of model simulations, the results of which were in accordance with our bifurcation analysis. Based on our results, we can recommend both adaptive and gradient descent RED to improve the robustness of the RED protocol.

Congestion Control Mechanism Based on Dual Threshold DI-RED for WSNs

In wireless sensor networks (WSNs), traffic flow congestion can reduce network performance. We propose a congestion control method for WSNs that use a cache state with a dual threshold in the router buffer to control congestion. In addition, the congestion is controlled by the channel transmission state that is monitored by the queuing variation tendency and the transmission rate. Based on the active queue management mechanism, a dual threshold with the double index random early detection (DI-RED) model is established to improve network performance. We also present the congestion control algorithm of DI-RED model to ensure the perfect actualization. By solving the DI-RED model, a series of channel indicators is obtained, including packet loss rate, average queue length, delay and throughput. Simulation results show that the proposed DI-RED model is more stable and demonstrates better control, which can overcome the parameter sensitivity of the RED mechanism. The proposed model realises a reasonable tradeoff between network performance and packet dropping probability, which effectively avoids degraded network performance caused by pursuing a lower packet dropping probability. The results are important when selecting a congestion control mechanism and parameter settings for WSNs.

Design of Network Traffic Congestion Controller with PI AQM Based on ITAE Index

Establishing the proper values of controller parameters is the most important thing to design in active queue management (AQM) for achieving excellent performance in handling network congestion. For example, the first well known AQM, the random early detection (RED) method, has a lack of proper parameter values to perform under most the network conditions. This paper applies a Nelder-Mead simplex method based on the integral of time-weighted absolute error (ITAE) for a proportional integral (PI) controller using active queue management (AQM). A TCP flow and PI AQM system were analyzed with a control theory approach. A numerical optimization algorithm based on the ITAE index was run with Matlab/Simulink tools to find the controller parameters with PI tuned by Hollot (PI) as initial parameter input. Compared with PI and PI tuned by Ustebay (PIU) via experimental simulation in Network Simulator Version 2 (NS2) in five scenario network conditions, our proposed method was more robust. It provided stable performance to handle congestion in a dynamic network.

Periodic solutions, chaos and bi-stability in the state-dependent delayed homogeneous Additive Increase and Multiplicative Decrease/Random Early Detection congestion control systems

The combination of Additive Increase and Multiplicative Decrease (AIMD) congestion control and Random Early Detection (RED) queue as a whole congestion control system plays a key role in the overwhelming success of the Internet. Thus it is important to investigate the periodic oscillations and complicated dynamics of the state-dependent delayed homogeneous Additive Increase and Multiplicative Decrease/Random Early Detection (AIMD/RED) congestion control system and its modified version fully in this paper. Firstly employing the semi-analytical method called as the harmonic balance method with alternating frequency/time (HB-AFT) domain technique, the approximate analytical expressions of periodic solutions of the generalized homogeneous-flow Additive Increase and Multiplicative Decrease/Random Early Detection (AIMD/RED) system with state-dependent round-trip delay are considered. We compare them with the results of numerical simulations by WinPP, they agree very well with each other. It demonstrates that the method employed here is versatile, valid, simple and effective. Then to the end of improving its modeling and performance, we modify the above model by taking an easy approximate dropping function. Furthermore, for the modified delayed homogeneous system, the approximate analytical expressions of periodic solutions are obtained accurately, and some complex dynamics are also presented. Four kinds of bi-stability, i.e., the coexistence of chaos and Period-3 solution, that of Period-1 and Period-2 solutions, that of Period-2 and Period-2 solutions, that of Period-4 and Period-2 solutions are disclosed. And a route to chaos, i.e., Period Doubling bifurcation to chaos, and the window of Period-3 to chaos are also discovered. The periodic oscillation can reduce the link utilization, induce the TCP stream synchronization services and further congestion. Chaotic oscillation may result in collapse. Therefore, all complex dynamical phenomena found in this paper are harmful and should be avoided. The obtained results can be very helpful for the researchers to have a better understanding of the mechanism of the network congestion control system, and they can select the parameters properly to improve the network stability and performance.

Dynamic Queue Synchronization for Congestion Control Over Wireless Sensor Networks

Congestion control over resource constrained sensor network is a critical challenge and it is also quite hard to fulfill the QoS constraints for end users. In this paper, a dynamic queue synchronization (DQS) scheme is introduced that can alter the data rate dynamically as per the current buffer length. Its performance is compared with existing schemes i.e., Dynamic Round Robin (DRR) and Random Early Discard (RED) using different routing protocols i.e., LEACH/PEGASIS/TEEN under various constraints (Throughput/End-to-End Delay/Routing Load/Energy).

An Exponential Active Queue Management Method Based on Random Early Detection

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.

Estimating an Additive Path Cost With Explicit Congestion Notification

Network utility maximization is a well-accepted theoretical framework that describes how congestion controls cooperate to achieve an ideal sending rate allocation, for given utility functions of senders and constraints of the network. These network constraints are expressed as a “cost” in the framework. In practice, most congestion control mechanisms obtain feedback that is different from the framework's “cost.” This article focuses on explicit congestion notification (ECN), which has been shown to be advantageous when it is available, e.g., with the popular datacenter transmission control protocol mechanism. However, different from the framework's cost, ECN marks are not additive. We present a practical solution to this problem; it changes how end hosts interpret the ECN signal, while for routers, a special configuration of random early detection is used.

A congestion-aware decision-driven architecture for information-centric Internet-of-Things applications

This paper introduces a decision-driven architecture for the information-centric Internet-of-Things (IoT) scenarios. The hallmark of this work is the conjugate of the awareness about the congestion situations along with the quality of the decisions’ supporting information. Controlling the incoming decisions’ upsurge is accomplished on a fashion that respects their degree of emergency. Wherefore, the Active Weighted Random Early Detection (AWRED) algorithm is integrated into the autonomic admission module to underpin the congestion control concept. Afterwards, an adaptive information-centric scheduling module is proposed in order to devise an optimized information-aware execution schedule for the admitted real-time decisions. The proposed scheduling algorithm is augmented with a Global best Harmony search (GHS) based amending approach. This integrated scheduling concept empowers the proposed algorithm from tuning the schedules of the admitted real-time decisions such that the completeness opportunities of decisions are elevated without sacrificing the rest architecture objectives. Simulations results demonstrate the robustness of the proposed architecture performance against various congestion degrees, an essential feature in a highly dynamic environment as the IoT. Furthermore, it has been proved that the proposed architecture significantly surpasses state-of-art approaches in terms of both quality of information (QoI) and quality of service (QoS) obligations especially during congested situations.

Influencing Performance Measurements through Varying Packet Capacities of Queue Nodes - DRED

This paper explores the impact of changes to the packet capacities of queue nodes on the performance of congestion algorithms. The dynamic random early drop (DRED) algorithm is crucial in managing congestion, predicting it, and selecting the optimal packet number. Two performance measures were employed to evaluate the effect of varying packet capacities, namely throughput and average queuing delay. Plasticity and efficacy of varying capacities of queue nodes was examined, and varying packet capacities of queue nodes were compared to assess performance. The first queue node performed more effectively than the second queue node in terms of throughput, while the second queue node outperformed the third queue node on both measures.

Nonlinear performance evaluation model for throughput of AQM scheme using full factorial design approach

SummaryThe performance of active queue management (AQM) is measured in terms of throughput, delay, queue size, and loss rate. We have carried out the optimized performance measure of throughput for AQM scheme random early detection (RED) using full factorial design (FDD) technique that is a new approach of performance analysis particularly for congestion control algorithms. We have considered the input factors, viz, buffer size, maximum threshold, and the number of file transfer protocol (FTP) sources for the evaluation of RED that can be used for other AQM schemes, viz, adaptive RED, three‐section RED (TRED), and adaptive queue management with random dropping (AQMRD). The effect of each input factor as well as their interactions are evaluated using factorial design technique that results to obtain the nonlinear equation for performance measure in terms of input factors buffer size, maximum threshold, and the number of FTP sources. Finally, we show the contour plots for variation of performance measure throughput (steady state) from minimum to maximum values with respect to the different setting of input parameters.

Active Queue Management Techniques for Congestion Control in TCP Communication Networks: New Prospective

the computer network area has grown very fast from previous years, as a result of which the control of traffic load in the network is at a higher priority. In network, congestion occurs if numbers of coming packets exceed, like bandwidth allocation along with buffer space. This might be due to poor network performance in terms of throughput, packet loss rate, and average packet queuing delay. For enhancing the overall performance when this network will become congested, numerous exclusive aqm (active queue management) techniques were proposed and few are discussed in this research paper. Particularly, aqm strategies are analyzed in detail as well as their obstacles along with strengths are emphasized. There are several algorithms which are under the aqm like ared, fred, choke, red (random early detection), blue, stochastic fair blue (sfb), random exponential marking (rem), svb, raq, etc.

Traffic Queuing Management in the Internet of Things: An Optimized RED Algorithm Based Approach

Congestion control is one of the main obstacles in cyberspace traffic. Overcrowding in internet traffic may cause several problems; such as high packet hold-up, high packet dropping, and low packet output. In the course of data transmission for various applications in the Internet of things, such problems are usually generated relative to the input. To tackle such problems, this paper presents an analytical model using an optimized Random Early Detection (RED) algorithm-based approach for internet traffic management. The validity of the proposed model is checked through extensive simulation-based experiments. An analysis is observed for different functions on internet traffic. Four performance metrics are taken into consideration, namely, the possibility of packet loss, throughput, mean queue length and mean queue delay. Three sets of experiments are observed with varying simulation results. The experiments are thoroughly analyzed and the best packet dropping operation with minimum packet loss is identified using the proposed model.

Controlling Delay at the Router Buffer using Modified Random Early Detection

Controlling Delay at the Router Buffer using Modified Random Early Detection

Performance Evaluation of TCP Vegas over TCP Reno and TCP NewReno over TCP Reno

In the Transport layer, there are two types of Internet Protocol are worked, namely- Transmission Control Protocol (TCP) and User datagram protocol (UDP). TCP provides connection oriented service and it can handle congestion control, flow control, and error detection whereas UDP does not provide any of service. TCP has several congestion control mechanisms such as TCP Reno, TCP Vegas, TCP New Reno, TCP Tahoe, etc. In this paper, we have focused on the behavior performance between TCP Reno and TCP Vegas, TCP New Reno over TCP Reno, when they share the same bottleneck link at the router. For instigating this situation, we used drop-tail and RED algorithm at the router and used NS-2 simulator for simulation. From the simulation results, we have observed that the performance of TCP Reno and TCP Vegas is different in two cases. In drop tail algorithm, TCP Reno achieves better Performance and throughput and act more an aggressive than Vegas. In Random Early Detection (RED) algorithm, both of congestion control mechanism provides better fair service when they coexist at the same link. TCP NewReno provides better performance than TCP Reno.

Congestion Control in CoAP Observe Group Communication.

The Constrained Application Protocol (CoAP) is a simple and lightweight machine-to-machine (M2M) protocol for constrained devices for use in lossy networks which offers a small memory capacity and limited processing. Designed and developed by the Internet Engineering Task Force (IETF), it functions as an application layer protocol and benefits from reliable delivery and simple congestion control. It is implemented for request/response message exchanges over the User Datagram Protocol (UDP) to support the Internet of Things (IoT). CoAP also provides a basic congestion control mechanism. In dealing with its own congestion, it relies on a fixed interval retransmission timeout (RTO) and binary exponential backoff (BEB). However, the default CoAP congestion control is considered to be unable to effectively perform group communication and observe resources, and it cannot handle rapid, frequent requests. This results in buffer overflow and packet loss. To overcome these problems, we proposed a new congestion control mechanism for CoAP Observe Group Communication, namely Congestion Control Random Early Detection (CoCo-RED), consisting of (1) determining and calculating an RTO timer, (2) a Revised Random Early Detection (RevRED) algorithm which has recently been developed and primarily based on the buffer management of TCP congestion control, and (3) a Fibonacci Pre-Increment Backoff (FPB) algorithm which waits for backoff time prior to retransmission. All the aforementioned algorithms were therefore implemented instead of the default CoAP mechanism. In this study, evaluations were carried out regarding the efficiency of the developed CoCo-RED using a Cooja simulator. The congestion control mechanism can quickly handle the changing behaviors of network communication, and thus it prevents the buffer overflow that leads to congestions. The results of our experiments indicate that CoCo-RED can control congestion more effectively than the default CoAP in every condition.

Sensitivity analysis of queue‐based AQM over network parameters

The comparative analysis of the active queue management (AQM) has been seen in the past work, while the comparative analyses for combined multiple output responses or performance parameters viz., throughput, delay, and loss rate are rarely seen in the literature. The comparative analysis using single variable comprising the output performance parameters has been attempted. The sensitivity of the network parameters over the throughput, delay, and loss rate for different AQM schemes has been measured. An attempt has also been made to stabilise the AQM algorithm that aims to achieve a low-loss rate and a high throughput. The models for the stabilisation of queue size of random early detection (RED) based on the network parameter have been studied. The comparative performance analysis of existing stabilised models has also been presented with author's modified RED. The key idea is to modify the existing RED algorithm to achieve stabilisation in queue length at routers with the reduced loss rate as compared to RED.

IGRED: An Improved Gentle Random Early Detection Method for Management of Congested Networks

Controlling congested router buffers of a network has a crucial role in improving network’s performance. This paper proposes a novel Active Queue Management (AQM) method named Improved Gentle Random Early Detection (IGRED) that based on GRED algorithm, which counted as one of the popular AQM methods. The proposed is mainly developed to overcome the problems faced with classic GRED. The initial packet-dropping probability depends on several parameters such as the average queue length, maximum value of packet dropping probability, minimum and maximum thresholds, etc. IGRED reduces its reliance on the GRED’s parameters through shrinking these parameters. The results shows, when congestion is taken place, the proposed IGRED provides more satisfactory performance with reference to mean queue length, average queuing delay, and overflow packet loss probability.

Queue models for wireless sensor networks based on random early detection

Wireless sensor networks (WSNs) are widely used in many fields of industry, agriculture, national defense, and technology. Their performance has a profound impact on their application. The storage and forwarding of WSN network packets is a typical queuing system. Based on queuing theory, this paper studies the performance analysis models of the WSN node, constructs a WSN node queuing model based on M/M/1, a WSN node queuing model with priority, a WSN node M/M/1 queuing model based on random early detection (RED) and a WSN node M/M/1 queuing model with priority based on RED. The models are analyzed theoretically and the expressions of the average delay and queue length are mathematically derived. The experimental results are consistent with the theoretical analysis. Differentiating the packets into different priorities can improve the quality of service of the network. Introducing the RED congestion control and packet discarding policy can effectively solve the congestion problem.

The impact of global on-line information provision on transport networks and how random early detection can help

Undoubtedly real-time traffic information strongly influences individual and group driver behaviour. To a lay person, the perceived possible effect of additional (accurate) information is likely to appear unambiguously beneficial. However, just as Braess and Down-Thomson, paradoxes for the addition of road infrastructure, more is not always better. It seems likely that similar negative effects can also occur with the addition of information to route planning. In this paper we show three related results. First, it is indeed the case that highly accurate real-time traffic information can have negative effects on drivers individually and altogether. Secondly, we show that these negative effects specifically arise due to the creation of oscillations in traffic pattern which exacerbate small occurrences of otherwise benign traffic congestion. Lastly, we show that novel adoptions of Random Early Detection strategies dampen such oscillations making real-time information a benefit to outcomes rather than a potential threat.