Active Queue Management Method Research Articles

Fuzzy-Based Active Queue Management Using Precise Fuzzy Modeling and Genetic Algorithm

Active Queue Management (AQM) methods significantly impact the network performance, as they manage the router queue and facilitate the traffic flow through the network. This paper presents a novel fuzzy-based AQM method developed with a computationally efficient precise fuzzy modeling optimized using the Genetic Algorithm. The proposed method focuses on the concept of symmetry as a means to achieve a more balanced and equitable distribution of the resources and avoid bandwidth wasting resulting from unnecessary packet dropping. The proposed method calculates the dropping probability of each packet using a precise fuzzy model that was created and tuned in advance and based on the previous dropping probability value and the queue length. The tuning process is implemented as an optimization problem formulated for the b0, b1, and b2 variables of the precise rules with an objective function that maximizes the performance results in terms of loss, dropping, and delay. To prove the efficiency of the developed method, the simulation was not limited to the common Bernoulli process simulation; instead, the Markov-modulated Bernoulli process was used to mimic the burstiness nature of the traffic. The simulation is conducted on a machine operated with 64-bit Windows 10 with an Intel Core i7 2.0 GHz processor and 16 GB of RAM. The simulation used Java programming language in Apache NetBeans Integrated Development Environment (IDE) 11.2. The results showed that the proposed method outperformed the existing methods in terms of computational complexity, packet loss, dropping, and delay. As such, in low congested networks, the proposed method maintained no packet loss and dropped 22% of the packets with an average delay of 7.57, compared to the best method, LRED, which dropped 21% of the packets with a delay of 10.74, and FCRED, which dropped 21% of the packets with a delay of 16.54. In highly congested networks, the proposed method also maintained no packet loss and dropped 48% of the packets, with an average delay of 16.23, compared to the best method LRED, which dropped 47% of the packets with a delay of 28.04, and FCRED, which dropped 46% of the packets with a delay of 40.23.

A Fully Adaptive Active Queue Management Method for Congestion Prevention at the Router Buffer

A Fully Adaptive Active Queue Management Method for Congestion Prevention at the Router Buffer

Evaluation of Active Queue Management Methods based on Integrated AHP, TOPSIS and Fuzzy TOPSIS

The Evaluation and selection of Active Queue Management (AQM) methods are a complicated and challenging task. Improper AQM method can cause up-mark and network malfunctioning. To achieve satisfactory performance, various evaluation criteria need to be considered. In order to find the limitations of how the criteria are determined, there is a need to find out how their procedures change according to the evaluation and benchmarking process of AQM. This article focuses on the evaluation and benchmarking of Active Queue Management methods using Multi-Criteria Decision-Making (MCDM) techniques. MCDM uses different techniques to figure out the best alternative from multi-criteria and multi alternative conditions. Analytic Hierarchy Process (AHP) is a multi-criteria decision-making technique that is used to assign weights to criteria. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used for ranking and selection of different alternatives by using distance measures. Whereas, FUZZY TOPSIS is used for criteria weightage and alternatives rating. MCDM calculation is performed on AQM methods and it is tried to discuss all these techniques. MCDM uses performance, overhead, and configuration criteria to evaluate and select the best AQM method that helps to determine solutions for future directions. The results show that Random Early Detection (RED) method got higher ranking score records as compared to other AQM methods.

Benchmarking of AQM methods of network congestion control based on extension of interval type-2 trapezoidal fuzzy decision by opinion score method

This study presents a benchmarking and evaluation approach for active queue management (AQM) network congestion control methods, which are considered as a problem of multi-criteria decision-making (MCDM). In recent years, the development of MCDM methods has been studied from various perspectives. The latest one called fuzzy decision by opinion score method (FDOSM) has proved its efficiency in solving the concerns faced by other methods. However, the approach of FDOSM and its extension is based on fuzzy type-1, which suffers from issues, especially minimising the effect of data uncertainties. Therefore, this study extended FDOSM into a fuzzy type-2 environment that utilises interval type-2 trapezoidal (IT2T) membership, and then discusses the effectiveness of such membership on AQM method benchmarking. The methodology of this study involves two consecutive phases. The first phase is the construction of a decision matrix utilised in AQM method benchmarking based on a list of AQM methods and multiple evaluation criteria. The second phase is regarding the new method (IT2T-FDOSM), which illustrated two main stages, namely, data transformation unit and data processing. The findings of this study are the following: (1) Individual results of benchmarking which used six decision-makers are almost similar, with the AQM fuzzy GRED method ranked as the best. (2) The group benchmarking results show that a relatively similar order and fuzzy GRED method is the best as well. (3) IT2T-FDOSM can deal with the uncertainty problem properly. (4) The results show significant differences amongst the groups’ scores, which indicate the validity of the IT2T-FDOSM results.

Multidimensional Benchmarking Framework for AQMs of Network Congestion Control Based on AHP and Group-TOPSIS

This paper aims to propose a grouping framework for benchmarking the active queue management (AQM) methods of network congestion control based on multicriteria decision-making (MCDM) techniques to assist developers of AQM methods in selecting the best AQM method. Given the current rapid development of the AQM techniques, determining which of these algorithms is better than the other is difficult because each algorithm performs better in a specific metric(s). Current benchmarking studies benchmark the AQM methods from a single incomplete prospective. In each proposed AQM method, the benchmarking was achieved with reference to some evaluation measures that are relatively close to the desired goal being followed during the development of the AQM methods. Furthermore, the benchmarking frameworks of AQM methods are complicated and challenging because of the following reasons: (1) the technical details of the AQM methods are adapted and the input parameters are selected according to the sensitivity of the AQM methods; and (2) a framework is developed and designed for simulating AQM methods, the simulated network and the collected results. For this purpose, a set of criteria for AQM comparison are determined. These criteria are performance, processing overhead and configuration. The benchmarking framework is developed based on the crossover of three groups of multi-evaluation criteria and several AQM methods as a proof of concept. The AQM families that are implemented and utilized in experiments to generate the data that are used as a proof of concept of our proposed framework are the parameter-based (pars) and fuzzy-based AQM methods. Accordingly, constructing the decision matrix (DM) that will be used to generate the final results is necessary. Subsequently, the underlying AQM methods are benchmarked and ranked using MCDM techniques, namely, integrated analytical hierarchy process (AHP) and technique for order of preference by similarity to ideal solution (TOPSIS). The validation was performed objectively. The [Formula: see text] deviation was computed to ensure that the AQM methods ranking undergo systematic ranking. Results illustrate that (1) the integration of AHP and TOPSIS solves the AQM method benchmarking problems; (2) results of the individual TOPSIS context clearly show variances among the ranking results of the six experts; (3) the ranks of the AQM methods obtained from internal and external TOPSIS group decision-making are nearly similar, with random early detection method being ranked as the best one; and (4) in the objective validation, significant differences were found between the groups’ scores, thereby indicating that the ranking results of internal and external TOPSIS group decision-making were valid.

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.

Congestion tracking control for multi-router TCP/AQM network based on integral backstepping

With the rapid development of the Internet of things and mobile Internet, more and more data flows into the Internet and will cause congestion of multiple routers in the network. However, most of the current congestion control methods are designed for a single router, which cannot adapt to the situation of multi-router congestion. Therefore, this paper proposes a congestion control algorithm to solve the network congestion problem of multiple routers. The model used in this paper captures the dynamics of Transmission Control Protocol (TCP) and covers the four possible behaviors of the TCP network when congestion occurs. The idea of TCP Active Queue Management (AQM) is to reduce congestion by actively discarding some packets in the queue. Taking the probability of packets marked as dropping as the control input, the congestion problem can be solved by using the backstepping method to make the queue length converge to the desired queue length. In this paper, the classical control strategy, backstepping, is extended to TCP network with multiple bottleneck routers to handle the AQM problem. Inspired by the existing network model with one bottleneck router, a revised model is introduced for TCP/AQM network with multiple bottleneck routers. And then, an AQM method Backstepping Congestion Control for Multi-router (BCCM) is proposed to deal with network congestion based on integral backstepping. Simulation results demonstrate that the convergence time of BCCM is about 10% of that of RED and The packet loss ratio of BCCM is 3 orders of magnitude lower than RED. In addition, BCCM has greater stability when traffic, link capacity, and router locations change.

The results of the experimental study of the Active Queue Management method at the interfaces of telecommunication networks

he paper presents the results of an experimental study of the active queue management method at the interfaces of telecommunication networks. Experimental adequacy and assessment of the effectiveness of the method from the point of view of the main quality of services indicators were carried out at the base of the Cisco Systems laboratory. As a load testing package, the D-ITG package was used to generate and further analyze network traffic. In order to verify the interface bandwidth allocation solutions obtained during the application of the method, they were compared with the WFQ scheduling mechanisms for the average packet delay. To verify the obtained solutions using the active queue management method, they were compared with the AQM WRED mechanisms according to the probability of packet loss. The results of the investigation confirmed the effectiveness of the active queue management method in terms of improving the average packet delay — from 12-17% to 22-25% for high-priority flows (EF, AF41-43) and from 8-12% to 16-19% for low priority flows (AF11-13). Using the active queue management method reduced the probability of packet loss by 7-12% for high-priority flows (EF, AF41-43) and by 10-17% for low-priority flows (AF11-13). It has been established that the recommended area of the practical application of the proposed models for queuing and bandwidth management is the areas of high congestion (more than 80-85%) and congestion of interfaces of telecommunication networks routers, especially in conditions of increased dynamics of their state change.

Multi-Criteria Evaluation and Benchmarking for Active Queue Management Methods: Open Issues, Challenges and Recommended Pathway Solutions

The evaluation and benchmarking processes of active queue management (AQM) methods are complicated and challenging. Several evaluation criteria/metrics must be considered before an AQM method can yield satisfactory performance using specific metric(s). Further investigations are required to highlight the limitations of how criteria/metrics are determined and how their procedures accord with the evaluation and benchmarking processes of AQM. In this paper, we presented comprehensive insights into the multi-criteria evaluation and benchmarking of AQM methods based on two critical directions. First, current AQM evaluation criteria are collected, analyzed and categorized. Second, these AQM evaluation criteria highlight conflicting issues and benchmarking techniques to identify weak points, and possible solutions are discussed. The findings of this study are as follows: (1) The limitations and problems of existing AQM evaluation and benchmarking methods, such as multi-evaluation criteria, criteria trade-off, benchmarking and criteria significance, are presented and emphasized. (2) Multi-criteria decision-making using multiple criteria, such as performance, processing overhead and configuration, can be used to benchmark numerous AQM methods to determine solutions for future directions.

Stabilizing Average Queue Length in Active Queue Management Method

This paper proposes the Stabilized (DGRED) method for congestion detection at the router buffer. This method aims to stabilize the average queue length between allocated minthre_shold and doublemaxthre_shold positions to increase the network performance. The SDGRED method is simulated and compared with Gentle Random Early Detection (GRED) and Dynamic GRED active queue management methods. This comparison is built on different important measures, such as dropping probability, throughput, average delay, packet loss, and mean queue length for packets. The evaluation aims to identify which method presents better simulation performance measurement results when non-congestion or congestion situations occur at the router buffers in congestion control. The results show that at high packet arrival probability, the proposed algorithm helps provide lesser queue length values, delayed time, and packet loss compared with current methods. Furthermore, SDGRED generates adequate throughput at high packet arrival probability.

Open shortest path first routing under random early detection

In this article, we consider a variant of Open Shortest Path First (OSPF) routing that accounts for Random Early Detection (RED), an Active Queue Management method for backbone networks. In the version of OSPF we consider in this article we only require a single network path be available between each origin and destination, a simplification of the OSPF protocol. We formulate a mixed integer non‐linear program to determine the data paths, referred to as a routing policy. We prove that determining an optimal OSPF routing policy that accounts for RED is NP‐Hard. Furthermore, in order for the generated routing policies to be real‐world implementable, referred to as realizable, we must determine weights for all arcs in the network such that solving the all‐pairs shortest path problem using these weights reproduces the routing policies. We show that determining if a set of all‐pairs routes is realizable is also NP‐Hard. Fortunately, using traffic data from three real‐world backbone networks, we are able to find realizable routing policies for these networks that account for RED, using an off‐the‐shelf solver, and policies found perform better than those used in each network at the time the data was collected.

Improving network congestion: A RED-based FuzzyPID approach

This research proposes a new active queue management method (or FPID) to improve network congestion, which integrates the random early detection (RED) method and fuzzy proportional integral derivative (FuzzyPID) approach. Fuzzy theory applied together with PID helps control the buffer queue within the target. This article provides the literature and theoretical background of network congestion problems, active queue management methods, automatic control systems, and fuzzy controllers. Following the discussion of the proposed FPID method and the experimental design, a simulation test is conducted and analyzed in comparison to the existing popular active queue management methods. The results prove the superiority of the proposed method.

QoS 적응형 플로우 기반 Active Queue Management 알고리즘 및 성능분석

방송 통신의 융합에 따라 차세대 멀티미디어 서비스인 IPTV 서비스가 각광받고 있다. IPTV 서비스는 무한한 채널수용과 미디어의 확장, 품질이 보장되는 서비스의 형태로 발전되면서 기존 네트워크의 트래픽 증가와 서비스 특성에 맞는 품질제어 기술들이 요구되고 이에 따라 플로우별 서비스 품질 제어 기술이 필요하게 되었다. 이를 위해 IETF (Internet Engineering Task Force)에서도 인터넷 상에서 IP QoS를 제공하기 위한 연구가 활발히 이루어지고 있는데 그 중 버퍼 관리 메커니즘으로 RED(Random Early Detection) 알고리즘 및 RED를 변형한 여러 가지 방법들이 제시되고 있으나 다양한 멀티미디어 서비스의 QoS 요구 사항을 충족시키는데 어려움이 있다. 이에 따라, 본 논문에서는 다양한 QoS를 요구하는 인터넷 멀티미디어 서비스를 지원하기 위해 각 서비스가 요구하는 목표 패킷 손실률을 유지시킬 수 있는 QoS적응형 플로우 기반 AQM (Active Queue Management) 알고리즘을 제안하고 ns-2를 이용한 시뮬레이션을 통해 성능 분석을 제시하였다. Due to the convergence of broadcasting and communications, IPTV services are spotlighted as the that next-generation multimedia services. IPTV services should have functionality such as unlimited channel capacity, extension of media, QoS awareness and are required increasing traffic and quality control technology to adapt the attributes of IPTV service. Consequently, flow based quality control techniques are needed. Therefore, many studies for providing Internet QoS are performed at IETF (Internet Engineering Task Force). As the buffer management mechanism among IP QoS methods, active queue management method such as RED(Random Early Detection) and modified RED algorithms have proposed. However, these algorithms have difficulties to satisfy the requirements of various Internet user QoS. Therefore, in this paper we propose the Flow based AQM(Active Queue Management) algorithm for the multimedia services that request various QoS requirements. The proposed algorithm can converge the packet loss ratio to the target packet loss ratio of required QoS requirements. And we present a performance evaluation by the simulations using the ns-2.

Predictive functional control for active queue management in congested TCP/IP networks

Predictive functional control (PFC) as a new active queue management (AQM) method in dynamic TCP networks supporting explicit congestion notification (ECN) is proposed. The ability of the controller in handling system delay along with its simplicity and low computational load makes PFC a privileged AQM method in the high speed networks. Besides, considering the disturbance term (which represents model/process mismatches, external disturbances, and existing noise) in the control formulation adds some level of robustness into the PFC-AQM controller. This is an important and desired property in the control of dynamically-varying computer networks. In this paper, the controller is designed based on a small signal linearized fluid-flow model of the TCP/AQM networks. Then, closed-loop transfer function representation of the system is derived to analyze the robustness with respect to the network and controller parameters. The analytical as well as the packet-level ns-2 simulation results show the out-performance of the developed controller for both queue regulation and resource utilization. Fast response, low queue fluctuations (and consequently low delay jitter), high link utilization, good disturbance rejection, scalability, and low packet marking probability are other features of the developed method with respect to other well-known AQM methods such as RED, PI, and REM which are also simulated for comparison.

Adaptive neural queue management for TCP networks

Active Queue Management (AQM) is a proven strategy to efficiently maintain queues and ensure high utilization of Transmission Control Protocol (TCP) network resources. The fundamental mechanism is to manage incoming packet rates at a router to prevent incipient network congestion. In this paper, we present an efficient neural network AQM system as a queue controller. The recurrent neural network has a Multi-layer Perceptron-Infinite Impulse Response (MLP-IIR) structure. Three distinct neural AQMs are trained under different network scenarios involving traffic levels. Selecting one of three neural AQMs is based on posterior probability history of traffic level. In addition, we investigate stochastic modeling of the network dynamics by a Dynamic Bayesian Network (DBN). This model allows implementation of a predictive AQM system in which queue dynamics are predicted and used for error prediction via online DBN estimation. Our AQM method is evaluated through simulation experiments both using an Ordinary Differential Equation (ODE) network model and using OPNET ©. The simulation results demonstrate that our adaptive neural AQM outperforms Random Early Detection (RED) and Proportional-Integral-Derivative (PID) based AQM.

An Intelligent Packet Dropping Algorithm with ECN Capability

As an effective supplement to end-to-end congestion control mechanism, active queue management aims to keep high link utility while maintaining low queuing delay. As an effective mechanism, the FIPD (fuzzy intelligent packet dropping) algorithm provides a brand-new method for active queue management. However, the FIPD algorithm also has its intrinsic defects, such as high packet loss rate. The objective of this paper is to overcome the defects of FIPD, and a new active queue management method FIPE(FIPD with ECN) is also proposed. Firstly, in this paper, the algorithm FIPD is reviewed and analyzed. Then the advantages and disadvantages are also pointed out. In order to overcome the defects such as high packet loss rate, the well-known ECN mechanism is introduced, which greatly improves the successful packet transmission. At the same time, some amendments are employed and a new active queue management algorithm FIPE is developed. Finally, the validity of the new algorithm is verified by a series of simulations on NS2 simulator.