Adaptive Random Early Detection Research Articles

A Brief Review on Queue Management Systems for different Applications

Queue management technology helps to reduce actual and predicted customer wait times, improve customer satisfaction, and provide the data to your managers needed to further optimize service, Queue Management System (QMS)presents a viable solution for different applications. It is employed to manage lines in a queue area in a variety of circumstances and locales. The article discusses the concepts of Queue Management Systems for Hospitals, Satellite Networks Based on Traffic Prediction, using Deep Neural Networks (DNN). Managing high patient loads in tertiary care hospitals represents a significant challenge in streamlining health service delivery. At several hospital service locations, including the registration, lab, and bill payment counters, patients must frequently wait in line. In these circumstances, Queue Management Systems (QMS) offer a practical patient management option. Satellite Internet the Adaptive Random Early Detection (ARED) queue management algorithm is proposed to be improved by traffic prediction based on a dynamic triple exponential smoothing model, smoothing coefficient optimization of the model using a differential evolutionary algorithm, and a cubic function based on traffic prediction. Customers and the administrative staff of the company can use a queue management system that uses the Open-CV platform and CNN algorithm for image processing, together with real-time person detection and people count recording. This essay also discusses several techniques in relation to their applications. With services that have medium to lengthy waiting periods, the proposed method seeks to reduce customer discontent. Keyword : Adaptive random early detection, CNN image processing, cubic function, differential evolution algorithm, dynamic triple exponential smoothing model, System for Managing Hospitals.

Improving RED algorithm congestion control by using the Markov decision process

Congestion control plays an essential role on the internet to manage overload, which affects data transmission performance. The random early detection (RED) algorithm belongs to active queue management (AQM), which is used to manage internet traffic. The RED is used to eliminate weakness in default control of the Transport Control Protocol (TCP) drop-tail mechanism. The drawback of RED is parameter tuning, while adaptive RED (ARED) automatically adjusts these parameters. In this study, the suggested algorithm, the Markov decision process RED (MDPRED) uses the Markov decision process (MDP) to suitably adapt values for queue weight in the RED algorithm based on average queue length to enhance the performance of the traditional RED during TCP Slow Startup phase. This study is conducted based on fluctuations among the rate of service, queuing weight, and the mean queue length by using open-source network simulator NS3. The study shows efficient results by fluctuating end-to-end packet throughput and fast response to the inception of congestion in the network. The modified algorithm achieves a low level of drop packets by evaluating the results with other five algorithms, which is done by increasing the algorithm’s response when the average queue size becomes close to the maximum queue length threshold.

Adaptive Hurst-Sensitive Active Queue Management.

An Active Queue Management (AQM) mechanism, recommended by the Internet Engineering Task Force (IETF), increases the efficiency of network transmission. An example of this type of algorithm can be the Random Early Detection (RED) algorithm. The behavior of the RED algorithm strictly depends on the correct selection of its parameters. This selection may be performed automatically depending on the network conditions. The mechanisms that adjust their parameters to the network conditions are called the adaptive ones. The example can be the Adaptive RED (ARED) mechanism, which adjusts its parameters taking into consideration the traffic intensity. In our paper, we propose to use an additional traffic parameter to adjust the AQM parameters—degree of self-similarity—expressed using the Hurst parameter. In our study, we propose the modifications of the well-known AQM algorithms: ARED and fractional order and the algorithms based on neural networks that are used to automatically adjust the AQM parameters using the traffic intensity and its degree of self-similarity. We use the Fluid Flow approximation and the discrete event simulation to evaluate the behavior of queues controlled by the proposed adaptive AQM mechanisms and compare the results with those obtained with their basic counterparts. In our experiments, we analyzed the average queue occupancies and packet delays in the communication node. The obtained results show that considering the degree of self-similarity of network traffic in the process of AQM parameters determination enabled us to decrease the average queue occupancy and the number of rejected packets, as well as to reduce the transmission latency.

Queue Management Algorithm for Satellite Networks Based on Traffic Prediction

In connexion with the effect of the self-similar characteristic of satellite network service traffic on queueing performance, a prediction model with optimised triple exponential smoothing is first established in this paper. This model performs network traffic prediction based on the dynamic triple exponential smoothing model and optimises the smoothing coefficient of the model through the differential evolution algorithm; a cubic function based on traffic prediction is further proposed to improve the adaptive random early detection (ARED) queue management algorithm. Based on the network traffic prediction results and the ARED, this algorithm uses the cubic function to perform nonlinear processing on the packet drop probability function. The simulation results show that the prediction model with optimised triple exponential smoothing has a high prediction accuracy, and the improved ARED algorithm based on the cubic function of traffic prediction in the presence of data bursts in self-similar traffic. It can effectively reduce the packet loss rate and improve the throughput, so as to better control the network congestion caused by self-similar traffic in satellite network.

Evaluation of Random Early Detection and Adaptive Random Early Detection in Benchmark Scenarios

In this paper, we evaluate Random Early Detection (RED) and Adaptive RED (ARED) in Benchmark Scenarios as detailed in RFC 7928. RED is one of the early proposed AQM mechanisms, which attains high throughput and keeps average delay low. Moreover, ARED is an extension to RED which eliminates the parameter sensitivity to improve the performance of RED. The results indicate that RED outperforms ARED in scenarios with abrupt changes in traffic load. ARED is known to reduce the packet drops and therefore, in rest of the scenarios it can be observed that ARED outperforms RED.

Research on Virtual Channel Multiplexing Algorithm Based on Advanced Orbiting Systems

To address multiple data types and high speeds in a business satellite network based on the virtual channel multiplexing technology of advanced orbiting systems (AOSs), an AOS virtual channel multiplexing system model is established. AOS queue management problems and the constraint problems in virtual channel scheduling are analyzed. On this basis, a priority adaptive random early detection and vaccine updating immune genetic algorithm is proposed. In queue management, based on the scheme of a multi-queue sharing cache with a minimum allocation, the algorithm adaptively adjusts packet dropping probability according to the packet priority. Based on an immune genetic algorithm, virtual channel scheduling automatically updates the vaccine database according to the matching degree of the vaccines and population. Meanwhile, the algorithm efficiently combines queue management and virtual channel scheduling. Experimental results show that the algorithm has excellent evolution performance and can maintain smaller queue lengths, smaller packet loss rates, less delay for high-priority virtual channels, and also has preferable fairness.

Priority Station Based Queuing Approach to Improve Quality of Service in IEEE 802.11e EDCF

Objectives: To enhance the performance of EDCF for the higher priority traffic under heavy load condition by providing higher throughput to provide desired QoS. To achieve this, we propose a method called Priority Station Based Queuing (PSBQ). An enhancement called as The IEEE 802.11e Enhanced Distributed Coordination Function (EDCF) has been developed to fulfill the growing need for the Quality of Service (QoS) in IEEE 802.11. Methods/Statistical Analysis: In EDCF standard the prioritization applies to the stations based on First in First Out approach (FIFO). Here, the size of the contention window size is reset according to the static equation after successful transmission for each traffic class, resulting in the degradation of the performance. So, we proposed our PSBQ which is the modified version of the already existing mechanism known as PRED, which applies the priority to the stations based on Random Early Detection (RED) instead of FIFO in EDCF Mechanism. Finding: In our approach to enhance the throughput of high priority traffic class, a priority is obtained by to the station based on Adaptive Random Early Detection (ARED), which is an Active Queuing mechanism and tuning was perform. On the parameter of the PSBQ, according to the network conditions and lastly the revision of the contention window adjustment is applied dynamically. Application/Improvements: Simulations was performed using the OpnetSimulator to evaluate its performance and found the results to be better in achieving prioritized, good service differentiation regarding throughput for higher priority traffic class under heavy load conditions to provide a desirable QoS.

An Improved Algorithm of Nonlinear RED Based on Membership Cloud Theory

Concerning on the shortcoming and complexity of Random early detection (RED) algorithm in network congestion control, a new RED algorithm based on the Hemi-Rise Cloud model (CRED) was proposed, nonlinear packet loss strategy was used, and sensitivity and uncertainty of parameters were improved. As a result, queue length could be kept stable in the neighborhood of reference value. Network congestion was well controlled and network resource was used effectively. The stability of the algorithm was studied and the experimental results showed that the proposed algorithm could improve the stability, and had better performance than the RED and Adaptive RED (ARED) algorithms.

Performance study of Active Queue Management methods: Adaptive GRED, REDD, and GRED-Linear analytical model

Congestion control is one of the hot research topics that helps maintain the performance of computer networks. This paper compares three Active Queue Management (AQM) methods, namely, Adaptive Gentle Random Early Detection (Adaptive GRED), Random Early Dynamic Detection (REDD), and GRED Linear analytical model with respect to different performance measures. Adaptive GRED and REDD are implemented based on simulation, whereas GRED Linear is implemented as a discrete-time analytical model. Several performance measures are used to evaluate the effectiveness of the compared methods mainly mean queue length, throughput, average queueing delay, overflow packet loss probability, and packet dropping probability. The ultimate aim is to identify the method that offers the highest satisfactory performance in non-congestion or congestion scenarios. The first comparison results that are based on different packet arrival probability values show that GRED Linear provides better mean queue length; average queueing delay and packet overflow probability than Adaptive GRED and REDD methods in the presence of congestion. Further and using the same evaluation measures, Adaptive GRED offers a more satisfactory performance than REDD when heavy congestion is present. When the finite capacity of queue values varies the GRED Linear model provides the highest satisfactory performance with reference to mean queue length and average queueing delay and all the compared methods provide similar throughput performance. However, when the finite capacity value is large, the compared methods have similar results in regard to probabilities of both packet overflowing and packet dropping.

An EAODV routing approach based on DARED and integrated metric

Wireless Mesh Network (WMN) is a new high-capacity and high-speed distributed broadband access network. Because of supporting multi-band devices, WMN is suitable for different application in the Internet of Things. In order to meet the performance requirements of multimedia traffic transmission, the routing approach designed for WMN must be taken into account the combination of load balance and anti-interference. We propose an extended Ad hoc on demand distance vector (AODV, which is a peer-to-peer the reactive routing approach) routing approach which is based on distributed adaptive random early detection and integrated metric for WMN. We firstly introduce several typical WMN routing approaches, and then based on the optimization theorem and cross-layer design proposes an integrated metrics to improve AODV routing approach. The steps and example using convex programming method are presented to solve routing optimization problem. Finally the feasibility of the proposed algorithm is verified with Method simulation. Simulation and analysis results show that the proposed EAODV routing approach improves the performance of WMN significantly.

Adaptive mechanism-based congestion control for networked systems

In order to assure the communication quality in network systems with heavy traffic and limited bandwidth, a new ATRED (adaptive thresholds random early detection) congestion control algorithm is proposed for the congestion avoidance and resource management of network systems. Different to the traditional AQM (active queue management) algorithms, the control parameters of ATRED are not configured statically, but dynamically adjusted by the adaptive mechanism. By integrating with the adaptive strategy, ATRED alleviates the tuning difficulty of RED (random early detection) and shows a better control on the queue management, and achieve a more robust performance than RED under varying network conditions. Furthermore, a dynamic transmission control protocol–AQM control system using ATRED controller is introduced for the systematic analysis. It is proved that the stability of the network system can be guaranteed when the adaptive mechanism is finely designed. Simulation studies show the proposed ATRED algorithm achieves a good performance in varying network environments, which is superior to the RED and Gentle-RED algorithm, and providing more reliable service under varying network conditions.

Easing Congestion in Computer Networks using the Receiver-Window Modification (RWM) Scheme

Random Early Detection (RED), Adaptive RED (ARED), BLUE and other Active Queue Management (AQM) queues have been proposed to replace drop-tail queues for Transmission Control Protocol/Internet Protocol (TCP/IP) networks to ease network congestion. Together with the Exp licit Congestion Notification (ECN) scheme, they had shown some pro mise over the drop tail queues. However, the timeout mechanism or the duration of the reception of three duplicate acknowledgements (ACKs), due to early-dropped packets, delays the response time of TCP in reducing the network congestion. Moreover, using ECN has its downsides: i) its messages may get lost; and ii) TCP implementations at the source, router and the destination have to be ECN-co mpliant (which presents a significant problem in today's implementations). To minimize these problems, this paper presents a novel AQM modification scheme called Receiver-W indow Modification (RWM), wh ich can be used together with any AQM queue for congestion avoidance in packet switched networks, especially at ingress and gateway routers. RWM improves on the average queue sizes, one-way end-to-end packet delays, delay jitter, throughput and number of dropped packets. It also overcomes the dependency of these AQM queues on the queues of downstream routers since ECN messages may get dropped or delayed due to congestion at these routers. We carry out extensive NS2 simulat ions to show our results and to support our claims.

Adaptive active queue management controller for TCP communication networks using PSO-RBF models

Addressing performance degradations in end-to-end congestion control has been one of the most active research areas in the last decade. Active queue management (AQM) aims to improve the overall network throughput, while providing lower delay and reduce packet loss and improving network. The basic idea is to actively trigger packet dropping (or marking provided by explicit congestion notification (ECN)) before buffer overflow. Radial bias function (RBF)-based AQM controller is proposed in this paper. RBF controller is suitable as an AQM scheme to control congestion in TCP communication networks since it is nonlinear. Particle swarm optimization (PSO) algorithm is also employed to derive RBF parameters such that the integrated-absolute error (IAE) is minimized. Furthermore, in order to improve the robustness of RBF controller, an error-integral term is added to RBF equation. The results of the comparison with Drop Tail, adaptive random early detection (ARED), random exponential marking (REM), and proportional-integral (PI) controllers are presented. Integral-RBF has better performance not only in comparison with RBF but also with ARED, REM and PI controllers in the case of link utilization while packet loss rate is small.

Survey on Random Early Detection Mechanism and Its Variants

Active Queue Management (AQM) algorithms are an Implementing schemes, So that packets are transmitted with higher priority than others. Random Early Detection (RED) is the first active queue management algorithm proposed for deployment in TCP/IP networks. RED has some parameter tuning issues that need to be carefully addressed for it to give good performance under different network scenarios. Various algorithms come from RED such as Stabilized RED (SRED), Dynamic RED (DRED), Adaptive RED (ARED) and Flow RED (FRED) these algorithms control congestion by discarding packets with a load dependent probability whenever a queue in the network appear to be congested, this paper will introduced some features about RED and its variants.

CARED: Cautious Adaptive RED gateways for TCP/IP networks

Random Early Detection (RED) is a widely deployed active queue management algorithm that improves the overall performance of the network in terms of throughput and delay. The effectiveness of RED algorithm, however, highly depends on appropriate setting of its parameters. Moreover, the performance of RED is quite sensitive to abrupt changes in the traffic load. In this paper, we propose a Cautious Adaptive Random Early Detection (CARED) algorithm that dynamically varies maximum drop probability based on the level of traffic load to improve the overall performance of the network. Based on extensive simulations conducted using Network Simulator-2 (ns-2), we show that CARED algorithm reduces the packet drop rate and achieves high throughput as compared to RED, Adaptive RED and Refined Adaptive RED. Unlike other RED based algorithms, CARED algorithm does not introduce new parameters to achieve performance gain and hence can be deployed without any additional complexity.

On the impact of adaptive RED in IP networks transporting H.264/MPEG-4 AVC video streams

This paper investigate the impact on the QoS offered by IP networks that transport real-time H.264/MPEG-4 AVC video streams when applying adaptive random early detection (ARED) as an active queue management technique instead of traditional drop-tail. Simulation results indicate that the ARED technique reduces the loss of video packets but degrades a higher number of video frames if compared to the drop-tail technique, while showing a small qualitative gain regarding delay and jitter. From these results it may be reasonably concluded that H.264/MPEG-4 AVC video streams do not benefit from employing ARED technique as VoIP streams do.

A Fuzzy Multiobjective Particle Swarm Optimized TS Fuzzy Logic Congestion Controller for Wireless Local Area Networks

A Takagi-Sugeno Fuzzy Logic Congestion Detection (TSFLCD) mechanism is proposed for IEEE 802.11 wireless Local Area Networks. A Fuzzy Preference based Multi-Objective Particle Swarm Optimization (FPMOPSO) mechanism, for tuning the input membership functions and the output scalars, is also proposed. An online adaptation mechanism that finetunes the output scalars based on system dynamics is implemented. Compared to the Adaptive Random Early Detection (ARED) and the Mamdani inference based Fuzzy Logic Congestion Detection (FLCD) mechanisms, simulation results show that the TSFLCD mechanism leads to more than 40% reduction in packet loss rate. It also leads to more than 25% and up to 14% reductions in jitter and delay respectively for real time traffic. This work lays a foundation for the development of simple multiobjective fuzzy congestion controllers in wireless LANs.

An Improved ARED Algorithm for Congestion Control of Network Transmission

In order to achieve high throughput and low average delay in computer network, it is necessary to stabilize the queue length and avoid oscillation or chaos phenomenon. In this paper, based on Adaptive Random Early Detection (ARED), an improved algorithm is proposed, which dynamically changes the range of maximum drop probabilitypmaxaccording to different network scenarios and adjustspmaxto limit average queue sizeqavein a steady range. Moreover, exponential averaging weightwis adjusted based on linear stability condition to stabilizeqave. A number of simulations show that the improved ARED algorithm can effectively stabilize the queue length and perform better than other algorithms in terms of stability and chaos control.

The Effects of Active Queue Management and Explicit Congestion Notification on Web Performance

We present an empirical study of the effects of active queue management (AQM) and explicit congestion notification (ECN) on the distribution of response times experienced by users browsing the Web. Three prominent AQM designs are considered: the proportional integral (PI) controller, the random exponential marking (REM) controller, and adaptive random early detection (ARED). The effects of these AQM designs were studied with and without ECN. Our primary measure of performance is the end-to-end response time for HTTP request-response exchanges. Our major results are as follows. If ECN is not supported, ARED operating in byte-mode was the best performing design, providing better response time performance than drop-tail queueing at offered loads above 90% of link capacity. However, ARED operating in packet-mode (with or without ECN) was the worst performing design, performing worse than drop-tail queueing. ECN support is beneficial to PI and REM. With ECN, PI and REM were the best performing designs, providing significant improvement over ARED operating in byte-mode. In the case of REM, the benefit of ECN was dramatic. Without ECN, response time performance with REM was worse than drop-tail queueing at all loads considered. ECN was not beneficial to ARED. Under current ECN implementation guidelines, ECN had no effect on ARED performance. However, ARED performance with ECN improved significantly after reversing a guideline that was intended to police unresponsive flows. Overall, the best ARED performance was achieved without ECN. Whether or not the improvement in response times with AQM is significant, depends heavily on the range of round-trip times (RTTs) experienced by flows. As the variation in flows' RTT increases, the impact of AQM and ECN on response-time performance is reduced.We conclude that AQM can improve application and network performance for Web or Web-like workloads. In particular, it appears likely that with AQM and ECN, provider links may be operated at near saturation levels without significant degradation in user-perceived performance.

A Load-Adaptive Active Queue Management Algorithm

Random Early Detection (RED) is the active queue management (AQM) algorithm recommended by IETF. Unfortunately, it is identified that RED is difficult to configure its parameters and the average queue length of RED is closely related to the load level. ARED (adaptive RED) is the adaptive version of RED. ARED dynamically adjust maximum packet marking probability according to the average queue length to make average queue length stable, but it still suffers from unstable instantaneous queue length and performance degradation under dynamic traffic conditions. In this paper, the cause of such problems of ARED is analyzed and a load adaptive active queue management scheme called LARED (load adaptive RED) is proposed. LARED features in adapting the load level of bottleneck link as well as quick response to queue length dynamics. Analysis and simulation results show that, compared with ARED and other AQM algorithms, LARED brings more stable queue dynamics; while keeping high link utilization and low queuing delay, it presents good responsiveness and robustness in various traffic conditions.