Random Early Detection Algorithm Research Articles

Analisis Performa Jaringan Software Defined Networking dengan Algoritma Random Early Detection

In the management of SDN networks, the main challenges faced are traffic congestion and network overload. One of the solutions that has been implemented is the Random Early Detection (RED) algorithm. This research aims to analyze the performance of SDN networks with the implementation of the RED algorithm compared to SDN networks without the use of the RED algorithm. The results of the study show that the use of the RED algorithm (Random Early Detection) in SDN networks has a significant impact on Quality of Service (QoS) parameters, such as packet loss, throughput, and average delay. In high load conditions, there is a controlled increase in packet loss with RED as the number of hosts increases. The average packet loss with RED is 1.57% for 4 hosts, whereas without RED, it is only 1.4%. RED also has a positive impact on throughput, with an average throughput of 243.8 kbps for 4 hosts with RED, while without RED, it only reaches 124.3 kbps. Furthermore, the use of RED reduces the average delay in packet delivery. The average delay with RED is 3.8 ms for 4 hosts, whereas without RED, it reaches 5.92 ms.

A Priority-Based Self-Adaptive Random Early Detection Algorithm in IoT Gateways

Effective algorithms for queue management are crucial in place of guaranteeing maximum efficiency in gateway routers since network traffic continues to expand dramatically. An online researcher has suggested the Active Queue Management (AQM) strategy regarding the upcoming generation of gateway switches. The common active queue scheme remains (RED) Random Early Detection. Random early detection is susceptible to parameterization issues and lacks a self-adaptation mechanism. Several RED variants have been formed; nevertheless, variations in traffic load have an adverse effect on all of them. Due to the fact that each has a static drop pattern, to address the RED and its variation schemes, the SARED system, or the design of self-adaptive random early detection was created. But in order to prevent congestion, during the time when the queue length surpasses a present maximum threshold limit, SARED aggressively removes packets. This causes networks having a lot of traffic situations the average is expected to increase queue delay, so in those cases, SARED should be less aggressive. This paper develops a priority-based queuing congestion control method for IoT gateways to manage network congestion. Our method (priority-based algorithms) performs substantially better with regard to throughput, delay, and packet loss than the present methods of SARED. The outcomes of the conducted simulation experiments have shown that in scenarios with heavy traffic loads, priority-based self-adaptive random early detection (PSARED) has greatly decreased average queuing delay by 3%, minimized average throughput by 1%, and decreased the rate of packet loss by 10% in contrast to SARED.

Congestion Avoidance in TCP Based on Optimized Random Forest with Improved Random Early Detection Algorithm

Transmission control protocol (TCP) ensures that data are safely and accurately transported over the network for applications that use the transport protocol to allow reliable information delivery. Nowadays, internet usage in the network is growing and has been developing many protocols in the network layer. Congestion leads to packet loss, the high time required for data transmission in the TCP protocol transport layer for end-to-end connections is one of the biggest issues with the internet. An optimized random forest algorithm (RFA) with improved random early detection (IRED) for congestion prediction and avoidance in transport layer is proposed to overcome the drawbacks. Data are initially gathered and sent through data pre-processing to improve the data quality. For data pre-processing, KNN-based missing value imputation is applied to replace the values that are missing in raw data and [Formula: see text]-score normalization is utilized to scale the data in a certain range. Following that, congestion is predicted using an optimized RFA and whale optimization algorithm (WOA) is used to set the learning rate as efficiently as possible in order to reduce error and improve forecast accuracy. To avoid congestion, IRED method is utilized for a congestion-free network in the transport layer. Performance metrics are evaluated and compared with the existing techniques with respect to accuracy, precision, recall, specificity, and error, whose values that occur for the proposed model are 98%, 98%, 99%, 98%, and 1%. Throughput and latency are also evaluated in the proposed method to determine the performance of the network. Finally, the proposed method performs better when compared to the existing techniques and prediction, and avoidance of congestion is identified accurately in the network.

AD-RED: A new variant of random early detection AQM algorithm

Intensive continuing research has been noticed among scholars in the literature with a particular appreciable interest in developing new enhanced variants for the long-standing Random Early Detection (RED) algorithm. Contemporary trends shows that researchers continue to follow a research line thereby exchanging the linear curve needed in RED with nonlinear curves. Several reports have shown that RED’s sole linear function is insufficiently powered for managing rising degrees of traffic congestion in the network. In this paper, Amended Dropping – Random Early Detection (AD-RED), a revised version of RED is presented. AD-RED algorithm consists in combining two nonlinear packet dropping functions: quadratic plus exponential. What’s more, results from ns-3 simulator shows that AD-RED reasonably stabilized and minified the (average) queue size; and obtained a whittled down end-to-end delay when compared with RED itself and another variant of RED. Hence, AD-RED is offered as a fully sufficient replacement for RED’s algorithm implementation in routers.

Developed the EX-RED Algorithm to Find Best Parameters Based on Load Connection Factor

The performance of the Internet is significantly impacted by network congestion. Because of the internet's current rapid growth, congestion could increase and cause more packets to be dropped. The Transmission Control Protocol (TCP) connection is used as the reliable transmission of packets which has a Drop Tail (DT) mechanism since congestion DT signals occur only when the queue has become full. The Random Early Detection algorithm mechanism can be developed as congestion control which eliminates the drawback of a tail drop queue full. The Red algorithm also has problems dealing with different numbers of connections due to fix parameter tuning. In this study, the EXhaustive search used with the RED algorithm to develop the EX-RED algorithm. Based on network performance metrics such as packet drop, delay, and throughput, the developed algorithm adjusts the default RED parameter to find the best of them. When the number of TCP connections changes, the exhaustive search used systematically enumerates all possible states of the parameter. The simulation results showed that the EXRED improved performance of the network as compared with the other five algorithms (GRED, RED, ARED, NLRED, and NLGRED) by decreasing delay and dropped packets and increasing packet throughput. Index Terms— AQM, Congestion Control, Exhaustive search, RED.

AmRED and RED-QE: redesigning random early detection algorithm

AmRED and RED-QE: redesigning random early detection algorithm

The Impact of Denial-of-Service Attacks and Queue Management Algorithms on Cellular Networks

In today's digital landscape, Distributed Denial of Service (DDoS) attacks stand out as a formidable threat to organisations all over the world. As known technology gradually advances and the proliferation of mobile devices, cellular network operators face pressure to fortify their infrastructure against these risks. DDoS incursions into Cellular Long-Term Evolution (LTE) networks can wreak havoc, elevate packet loss, and suboptimal network performance. Managing the surges in traffic that afflict LTE networks is of paramount importance. Queue management algorithms emerge as a viable solution to wrest control over congestion at the Radio Link Control (RLC) layer within LTE networks. These algorithms work proactively, anticipating, and mitigating congestion by curtailing data transfer rates and fortifying defences against potential DDoS onslaughts. In the paper, we delve into a range of queue management methods Drop-Tail, Random Early Detection (RED), Controlled Delay (CoDel), Proportional Integral Controller Enhanced (PIE), and Packet Limited First In, First Out queue (pFIFO). Our rigorous evaluation of these queue management algorithms hinges on a multifaceted assessment that encompasses vital performance parameters. We gauge the LTE network's resilience against DDoS incursions, measuring performance based on end-to-end delay, throughput, packet delivery rate (PDF), and fairness index values. The crucible for this evaluation is none other than the NS3 simulator, a trusted platform for testing and analysis. The outcomes of our simulations provide illuminating insights. CoDel, RED, PIE, pFIFO, and Drop-Tail algorithms emerge as top performers in succession. These findings underscore the critical role of advanced queue management algorithms in fortifying LTE networks against DDoS attacks, offering robust defences and resilient network performance.

A Triplex Region - Random Early Detection (TR-RED) Algorithm for Active Queue Management in Internet Routers

A Triplex Region - Random Early Detection (TR-RED) Algorithm for Active Queue Management in Internet Routers

Quadratic exponential random early detection: a new enhanced random early detection-oriented congestion control algorithm for routers

<span>Network congestion is still a problem on the internet. The random early detection (RED) algorithm being the most notable and widely implemented congestion algorithm in routers faces the problems of queue instability and large delay arising from the presence of an ineffectual singular linear packet dropping function. This research article presents a refinement to RED, named quadratic exponential random early detection (QERED) algorithm, which exploits the advantages of two drop functions, namely quadratic and exponential in order to enhance the performance of RED algorithm. ns-3 simulation studies using various traffic load conditions to assess and benchmark the effectiveness of QERED with two improved variants of RED affirmed that QERED offers a better performance in terms of average queue size and delay metrics at various network scenarios. Fortunately, to replace/upgrade the implementation for RED algorithm with QERED’s in routers will require minimal effort due to the fact that nothing more besides the packet dropping probability profile got to be adjusted.</span>

Improved random early detection congestion control algorithm for internet routers

In the internet, router plays a strategic role in the transmission of data packets. Active queue management (AQM) aimed at managing congestion by keeping a reduced average buffer occupancy and hence a minimal delay. The novel random early detection (RED) algorithm suffers from large average buffer occupancy and delay shortcomings. This problem is due in part to the existence of a distinctive linear packet drop function it deploys. In this paper, we present a new version of RED, called improved RED (IMRED). An important strategy of IM-RED is to deploy two dropping functions: i) nonlinear (i.e. quadratic) to deal with both light-and moderatenetwork traffic load conditions, and ii) linear to deal with heavy traffic load condition. Simulation experiments conducted using open-source ns-3 software to evaluate and compare the functionality of the proposed IM-RED with other two previous AQM algorithms confirmed that IM-RED reduces the average buffer occupancy and obtained an improved delay performance especially at heavy network traffic load scenario. Very fortunately, since RED algorithm is known to appear as a built-in model in ns-3 and even Linux kernel, its implementation can therefore be leveraged to obtain IMRED while only adjusting the packet dropping probability profile and holding on to its other attributes.

Research on the application of artificial intelligence in computer network technology in the era of big data

SummaryIn order to improve the security and reliability of computer network, especially the three aspects of fault network diagnosis overall model, computer network link fault detection and computer network congestion prediction, we propose a fuzzy neural network identification and prediction model. In addition, we use rough artificial neural network (RANN) network link mixture to manage all subnets in the network for unified remote allocation network, so as to improve the quality and robustness of the network. The results show that the RANN learning and recognition algorithm we introduced achieves the best performance with 85.12% reduction in total time and 86.82% reduction in the number of steps. In addition, the average error rate of RANN is reduced by 45.94% compared with Lagrangian neural storage algorithm. The iteration number of fuzzy neural network (FNN) neural network prediction model based on incremental rule extraction algorithm is reduced by 28.93%, and the packet loss rate of network peak shaving module controlled by FNN prediction model is reduced to 0.272%, which is 87.69% lower than that of random early detection algorithm. This is expected to provide better performance for the orderly control of network failure, data flow and network data transmission with higher load in the future, and further improve the security and reliability of computer networks.

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.

Random early detection-quadratic linear: an enhanced active queue management algorithm

This paper identifies the lone linear drop function for computing the dropping probability between certain queue threshold values as a major weakness for the random early detection (RED) algorithm as it leads to large delay and queue instability. To address this concern, we propose an enhanced RED-based algorithm called random early detection-quadratic linear (referred to as “RED-QL”) active queue management (AQM) which leveraged the benefit of a quadratic packet drop function for a light-to moderate traffic load conditions together with a linear packet drop function for a heavy traffic load condition respectively. Results from ns-3 network simulator using different experimental scenarios clearly reveals that the proposed RED-QL algorithm yields a substantial reduction in delay performance and indeed a reduced average queue size than other three representative AQM algorithms. RED-QL is robust, easy to implement and deploy in routers (both in software and hardware) as no more than the packet drop probability profile of the classic RED’s algorithm implementation needs modest alteration.

RED-LE: A Revised Algorithm for Active Queue Management

The random early detection (RED) algorithm was developed in 1993. Nearly three decades later, several improved variants have been proposed by scientists. The use of a (pure) linear function for computing packet drop probability has turned out to be a disadvantage, leading to the problem of large delays. Such a problem may be addressed by using linear and non-linear (i.e. as exponential) packet drop probability functions. This paper proposes a revised RED active queue management algorithm named RED-linear exponential (RED-LE). This variant involves an interplay of linear and exponential drop functions, in order to improve the performance of the original RED algorithm. More importantly, at low and moderate network traffic loads, the RED-LE algorithm employs the linear drop action. However, for high traffic loads, RED-LE employs the exponential function for computing the packet drop probability rate. Experimental results have shown that RED-LE effectively controls congestion and offers an improved network performance under different traffic loads.

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.

RED-I: A RED-Based Algorithm for Internet Routers

An essential goal of Active Queue Management (AQM) algorithm is to improve delay by keeping the average queue size low and maintain a high throughput. However, Random Early Detection (RED) and its various improved variants have not been able to achieve much in this regard. This paper is concerned with the problem of RED in terms of large delay resulting from RED’s inability to keep the average queue size small. Therefore, we propose a new RED-based AQM algorithm named RED-Improved (RED-I) which utilizes two linear packet dropping functions. ns-3 simulation experiments to compare the performance of the proposed RED-I with RED confirmed that RED-I outperformed RED in terms of delay especially at both light and heavy traffic load scenarios. Replacing/upgrading the RED algorithm implementations in Internet routers (either software or hardware) requires minimal effort since only the packet dropping probability profile needs to be adjusted.

Computer Network Dynamic Balance Flow Distribution Based on Closed-Loop Particle Swarm Feedback Model

Based on the closed-loop particle swarm feedback model, this paper proposes a graphical method to analyze the stability of the computer network dynamic balance system. First, based on the second-order time delay system model of congestion control, the stability of the system is described by characteristic pseudopolynomials. Secondly, based on the inverse line, the stability of the system is verified by graphical analysis methods, and the PID controller parameter range that guarantees the stability of the system is obtained, and the relationship between the controller proportional gain boundary and the network characteristic parameters is analyzed. Then, based on the analysis of the basic particle swarm optimization algorithm, the particle swarm evolution formula is divided into two parts, its own factors and social factors, and the influence of each part on the evolution speed and position of the particle swarm is analyzed, and an improved particle swarm is proposed. Finally, according to the above analysis, we find the corresponding equation from the appropriate solution in turn, thereby designing a class of particle swarm optimization algorithm with fewer intermediate variables. In view of the system involved in the classical PID control parameter tuning method, the improved particle swarm algorithm is applied to the parameter tuning and optimization of the PID controller. During the experiment, the improved PSO-PID controller optimization algorithm was used in the random early detection algorithm of active queue management, the process of the improved algorithm was researched and designed, and the relevant performance of the improved algorithm was verified through simulation experiments.

QLREDActive Queue Management Algorithm

The Random Early Detection (RED) algorithm has not been successful in keeping the average queue size low. In this paper, we an improved RED-based algorithm called QLRED which divides the dropping probability function of the RED algorithm into two equal segments. The first segment utilises a quadratic packet dropping function while the second segment deploys a linear packet dropping function respectively so as to distinguish between light and high traffic loads. The ns-3 simulation performance evaluations clearly showed that QLRED algorithm effectively controls the average queue size under various network conditions resulting in a low delay. Replacing/upgrading the RED algorithm in Internet routers requires minimal effort since only the packet dropping probability profile needs to be adjusted.

Hybrid modelling of the RED algorithm in the Julia language

This work is devoted to studying the possibilities of hybrid modeling of communication network algorithms in the Julia programming language. A simulation of the system, which consists of an incoming stream processed according to the Transmission Control Protocol (TCP) and a router processing traffic according to the Random Early Detection (RED) algorithm, is carried out. This model contains both continuous and discrete elements, for example, a discrete probability function of random packet dropping in the case of queue congestion.

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.