Active Queue Management Scheme Research Articles

Improving TCP performance over a common IoT scenario using the Early Window Tailoring method

The Internet of Things (IoT) is already a reality in homes, transforming the home network into a complex composition of different equipment where each one has its own attributes in terms of TCP protocol variants, transmission speed, reliability and delay. In home network scenarios where IoT devices are used, it is common to have a 802.11 environment with several devices sharing the same Access Point (AP). When TCP is used in this environment its performance is affected by many things, such as wireless link losses, queue saturation on access points, and fairness. To improve the TCP performance in these scenarios, this paper presents a new version of the Early Window Tailoring (EWT) approach. The EWT-IoT do not require any modification in the TCP, and it does not require that all routers in the path use the same Active Queue Management (AQM), the use in the AP is sufficient. To evaluate its performance, comparison tests against drop-tail, RED, ARED and EWA were performed. The EWT-IoT proved to be effective in congestion control, achieving greater performance than the other approaches, reducing the transfer time, having a superior goodput, maintaining a satisfactory fairness, and presenting no losses. Also, the EWT-IoT allowed the existence of a number of flows on average 65.2% better than its best competitor and 71.3% better when no AQM scheme was used.

Waiting Time in a General Active Queue Management Scheme

Waiting Time in a General Active Queue Management Scheme

Adaptive Active Queue Management based on Queue Ratio of Set-point Weighting

Presently, active queue management (AQM) is one of the important considerations in communication networks. The challenge is to make it simple and robust in bursty traffic and uncertain network conditions. This paper proposes a new AQM scheme, an adaptive ratio proportional integral (ARPI), for adaptively controlling network congestion in dynamic network traffic conditions. First, AQM was designed by adding a set-point weighting structure to a proportional integral (PI) controller to reduce the burstiness of network traffic. Second, an adaptive set-point weighting based on the ratio of instantaneous queue length to the set-point queue and the buffer size was proposed to improve the robustness of a non-linear network. The proposed design integrates the aforementioned expectations into one function and needs only one parameter change to adapt to fluctuating network condition. Hence, this scheme provides lightweight computation and simple software and hardware implementation. This approach was analyzed and compared with the PI AQM scheme. Evaluation results demonstrated that our proposed AQM can regulate queue length with a fast response, good stability under any traffic conditions, and small queuing delay.

Transmission Control Protocol Analysis Using NS3

The NS-3 Test Framework provides a focus on multidisciplinary development and high-level design, and is now being used by various experts around the world. Surprisingly, its Data Collection Protocol (DCP) implementation is late and is not planned to be used as a reference point for Transmission Control Protocol (TCP), focused research, where the Herald has provided NS-3 to accept NS-2 submissions. The latest-best configuration consists of a number of PCs connected to each other by two sweets and switches. As a result of the overlapping character, being a significant number of PCs, channels can create a complex problem. If a case of sensitivity is sent to the TCP bundles, each word flow should be taken with the appropriate treatment when there is a deadline. Drop-tail is a common scheme for using the power of measurement, and with this provision, the feeling cannot be limited to the illumination of long lines, which increases the delay in these methods. As a result, the flow efficiency decreases. The Board Active Queue Management (AQM) is largely oblivious to the fact that conspiracy and sensitivity are not the basic mechanisms that must be considered in solving this major problem. Another area to look for is depression, and the range of serious problems. In this work, a framework was developed based on drop- tail and different active queue management schemes such as DLP, Drop-Tail delivers, Packet First in First Out (PFIFO), Random Early Detection (RED) and Code Exploration Introduction. The evaluation is performed using an open-framework framework (NS-3) assessment framework. In the end, the results of the review suggest that Code and RED, a unique line of management figures, have shown much needed performance.

On the validity of numerical simulations for control-theoretic AQM schemes in computer networks

Active Queue Management (AQM) schemes complement Transmission Control Protocol (TCP) congestion-control mechanisms in reducing queuing delay, packet loss, and in improving network throughput. This paper presents a detailed mathematical review of the control-theoretic AQM feedback structure. We highlight and clarify the misconceptions regarding its usage, and furthermore, we verify and validate the numerical time-domain and event-domain simulations of existing AQM schemes using the MATLAB-Simulink platform and the NS-3 simulator platform respectively. Finally, conclusions and some future research directions are briefly discussed.

An intelligent scheme for congestion control: When active queue management meets deep reinforcement learning

With the explosive growth of data transmission requirements, the massive bursty traffic results in more frequent and serious network congestion. To deal with the burst traffic, the buffer size of the network is designed to be much larger than required. However, increasing buffer size leads to bufferbloat problem, which exacerbates network congestion. To address this problem, Active Queue Management (AQM) is proposed to cooperate with Transmission Control Protocol (TCP) congestion control mechanism. But it is hard to model dynamic AQM/TCP system and tune the parameters of conventional AQM schemes to obtain good performance under different congestion scenarios. In this paper, we aim to study the AQM scheme from a whole new perspective by leveraging emerging Deep Reinforcement Learning (DRL). A model-free approach, DRL-AQM, enables the AQM to learn the best dropping policy as human beings learn skills. DRL-AQM can capture intricate patterns in the data traffic model after trained in a simple network scenario, and leverage this to improve performance in a great variety of scenarios. There are two phases in our scheme, offline training phase and deployment phase. Once the model is trained, it can work well without any parameter-tuning in many scenarios. Experimental results show that (1) DRL-AQM outperforms conventional AQM algorithms in a variety of complex network scenarios and it is robust and insensitive to network conditions. (2) DRL-AQM keeps persistently low buffer capacity utilization without over dropping and damaging throughput. (3) DRL-AQM adapts automatically and continuously to the dynamics of the network links.

Stability of SDE-LJN System in the Internet to Mitigate Constant-Rate DDoS Attacks

The Internet is nowadays suffering dramatically serious attacks, with the distributed denial of service (DDoS) attacks being the representative and dominant ones. It is seen that, to stabilize the buffer queue length around a given target under DDoS attacks in the relevant routes is vitally important and helpful to mitigate the attacks and to improve the quality of service (QoS) for normal users. In the current paper, a stochastic queue dynamic model with L e ´ vy jump noise, which is affected by the continuous Brownian motion and the discontinuous Poisson process, is worked out to develop a novel and accurate mathematical framework for the stability of a route queue that deals with constant-rate DDoS attacks. This article proposes a security defensive mechanism in the network for solving the network collapse that can possibly be caused by DDoS attacks, otherwise. Particularly, based on the formulation of a stochastic queue dynamic with L e ´ vy jump noise, the mechanism that characterizes the behavior of the queue at routers is presented for stabilizing the queue length under constant-rate DDoS attacks. By applying the stochastic control theory into analyzing the performance of queue dynamic under constant-rate DDoS attacks, some explicit conditions are established under which the instantaneous queue length converges to any given target in a route. Simulation results demonstrate the satisfaction of the proposed defense mechanism with sharp contrast to the state of the art active queue management (AQM) schemes.

Intelligent Active Queue Management for Stabilized QoS Guarantees in 5G Mobile Networks

The fifth-generation (5G) new radio standard defines various functions for forwarding treatments under differentiated quality of services requirements. In particular, a gNB base station helps combat overbuffering due to massive packet flows from associated mobile devices. Several active queue management (AQM) schemes have been proposed previously, with controlled delay (CoDel) proving to be an efficient method that can properly handle excessive buffering, that is called bufferbloat. This article proposes a novel and enhanced AQM scheme based on CoDel for gNB that guarantees low queuing delays, minimizes packet drop rate, and furthermore stabilizes the built-in queue. According to data-intensive performance evaluation results, the proposed scheme verifies that the queuing delay for served packets are decreased and supports ultrareliable low latency communication applications. Especially, the proposed algorithm achieves the packet drop rates and queuing delays tradeoff as [ <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (1/V), O(V)] where V is a tradeoff parameter; providing efficient control decisions to improve performance in terms of time-average packet drop rate minimization subject to queue stability on demand in 5G mobile system.

Modeling a New AQM Model for Internet Chaotic Behavior Using Petri Nets

Formal modeling is considered one of the fundamental phases in the design of network algorithms, including Active Queue Management (AQM) schemes. This article focuses on modeling with Petri nets (PNs) a new scheme of AQM. This innovative AQM is based on a discrete dynamical model of random early detection (RED) for controlling bifurcations and chaos in Internet congestion control. It incorporates new parameters (α,β) that make possible better stability control over oscillations of an average queue length (AQL) at the router. The PN is validated through the matrix equation approach, reachability tree, and invariant analysis. The correctness is validated through the key properties of reachability, boundedness, reversibility, deadlock, and liveness.

SQM-LRU: A Harmony Dual-Queue Management Algorithm to Control Non-Responsive LTF Flow and Achieve Service Differentiation.

The increase in network applications diversity and different service quality requirements lead to service differentiation, making it more important than ever. In Wide Area Network (WAN), the non-responsive Long-Term Fast (LTF) flows are the main contributors to network congestion. Therefore, detecting and suppressing non-responsive LTF flows represent one of the key points for providing data transmission with controllable delay and service differentiation. However, the existing single-queue management algorithms are designed to serve only a small number of applications with similar requirements (low latency, high throughput, etc.). The lack of mechanisms to distinguish different traffic makes it difficult to implement differentiated services. This paper proposes an active queue management scheme, namely, SQM-LRU, which realizes service differentiation based on Shadow Queue (SQ) and improved Least-Recently-Used (LRU) strategy. The algorithm consists of three essential components: First, the flow detection module is based on the SQ and improved LRU. This module is used to detect non-responsive LTF flows. Second, different flows will be put into corresponding high or low priority sub-queues depending on the flow detection results. Third, the dual-queue adopts CoDel and RED, respectively, to manage packets. SQM-LRU intends to satisfy the stringent delay requirements of responsive flow while maximizing the throughput of non-responsive LTF flow. Our simulation results show that SQM-LRU outperforms traditional solutions with significant improvement in flow detection and reduces the delay, jitter, and Flow Completion Time (FCT) of responsive flow. As a result, it reduced the FCT by up to 50% and attained 95% of the link utilization. Additionally, the low overhead and the operations incur O(1) cost per packet, making it practical for the real network.

Deep Reinforcement Learning Based Active Queue Management for IoT Networks

Internet of Things (IoT) finds its applications in home, city and industrial settings. Current network is in transition to adopt fog/edge architecture for providing the capacity for IoT. However, in order to deal with the enormous amount of traffic generated by IoT devices and to reduce queuing delay, novel self-learning network management algorithms are required at fog/edge nodes. Active Queue Management (AQM) is a known intelligent packet dropping techique for differential QoS. In this paper, we propose a new AQM scheme based on Deep Reinforcement Learning (DRL) technique and introduce scaling factor in our reward function to achieve the trade-off between queuing delay and throughput. We choose Deep Q-Network (DQN) as a baseline for our scheme, and compare our approach with various AQM schemes by deploying them at the interface of fog/edge node. We simulated them by configuring different bandwidth and round trip time (RTT) values. The simulation results show that our scheme outperforms other AQM schemes in terms of delay and jitter while maintaining above-average throughput, and also verifies that AQM based on DRL is efficient in managing congestion.

SARED: A Self-Adaptive Active Queue Management Scheme for Improving Quality of Service in Network Systems

Considering the phenomenal growth of network systems, congestion remains a threat to the quality of service provided in such systems, hence, research on congestion control is still relevant. Internet research community regards Active Queue Management (AQM) as an effective approach to address congestion in network systems. Most of the existing AQM schemes possess static drop patterns and lack self-adaptation mechanism, as such don’t work well for networks where traffic load fluctuates. This paper proposes Self-Adaptive Random Early Detection (SARED) scheme which smartly adapts its drop pattern based on current network’s traffic load in order to maintain better and stable performance. In light to moderate load conditions, SARED operates in nonlinear modes in order to maximize utilization and throughput, while in high load condition, it switches to linear mode in order to avoid forced drops and congestion. Experiments conducted have revealed that regardless of traffic load’s condition, SARED provides optimal performance.

Federated Intelligence for Active Queue Management in Inter-Domain Congestion

Active Queue Management (AQM) has been considered as a paradigm for the complicated network management task of mitigating congestion by controlling buffer of network link queues. However, finding the right parameters for an AQM scheme is very challenging due to the dynamics of the IP networks. In addition, this problem becomes even more complex in inter-domain scenarios where several organizations interconnect each other with the limitation of not sharing raw and private data. As a result, existing AQM schemes have not been widely employed despite their advantages. Therefore, we present a solution that tackles the challenges of tuning the AQM parameters for inter-domain congestion control scenarios where the network management goes beyond an organization’s domain. We then introduce the Federated Intelligence for AQM (FIAQM) architecture, which enhances the existing AQM schemes by leveraging the Federated Learning approach. The proposed FIAQM framework is capable of dynamically adjusting the AQM parameters in a multi-domain setting, which is hard to achieve with the conventional AQM solutions working alone. To this end, FIAQM uses an artificial neural network, trained in a federated manner, to predict beyond-own-domain congestion and an intelligent AQM parameter tuner. The evaluation results show that FIAQM can effectively improve the performance of the inter-domain connections by reducing the congestion on their links while preserving the network data private within each participating domain.

On the Transient Queue with the Dropping Function.

We deal with a queueing system, in which arriving packets are being dropped with the probability depending on the queue size. Such a scheme is used in several active queue management schemes proposed for Internet routers. In this paper, we derive and analyze a selected transient characteristic of the model, i.e., the probability that in a given time interval the queue size is kept under a predefined level. As the main purpose of the discussed queueing scheme is to maintain the queue size low, this is a natural characteristic to study. In addition to that, the average time to reach a given level is derived. Theoretical results for both characteristics are accompanied by numerical examples. Among other things, they demonstrate that the transient behavior of the queue may vary significantly with the shape of the dropping function, even if the steady-state performance remains unaltered.

Interval type-2 and type-1 Fuzzy Logic Controllers for congestion avoidance in internet routers

Active queue management (AQM) scheme is an important research area in congestion control. The AQM mechanism attains the congestion controller by regularize the length of queue from evaluation or dropping the packets at the moderate routers. In this paper the interval type-2 and type-1 fuzzy logic controller are designed to desist the congestion in the networks. Several evolutionary optimization algorithms are used to select the parameters of two controllers as particle swarm optimization (PSO), social spider optimization (SSO) and ant colony Optimization (ACO). The simulation of linearized model of TCP/AQM are introduced in MATLAB (R2018a). Results of the PID like fuzzy type-1controller is compared with PID like interval type-2 fuzzy controller (IT2-FLC) based on optimization algorithms in each controller and shows that IT2-FLC with SSO has been given the best result.

Adaptive Active Queue Management based on Queue Ratio of Set-point Weighting

Presently, active queue management (AQM) is one of the important considerations in communication networks. The challenge is to make it simple and robust in bursty traffic and uncertain network conditions. This paper proposes a new AQM scheme, an adaptive ratio proportional integral (ARPI), for adaptively controlling network congestion in dynamic network traffic conditions. First, AQM was designed by adding a set-point weighting structure to a proportional integral (PI) controller to reduce the burstiness of network traffic. Second, an adaptive set-point weighting based on the ratio of instantaneous queue length to the set-point queue and the buffer size was proposed to improve the robustness of a non-linear network. The proposed design integrate s the a forementioned expectations into one function and needs only one parameter change to adapt to fluctuating network condition . Hence, t his scheme provides lightweight computation and simple software and hardware implement ation . This approach was analyzed and compared with the PI AQM scheme. Evaluation results demonstrated that our proposed AQM can regulate queue length with a fast response , good stability under any traffic conditions , and small queuing delay .

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.

An Adaptive Active Queue Management Based on Model Predictive Control

In recent years, active queue management (AQM) has gained more and more attention as an important part of network congestion control. Although there are many AQM algorithms, these algorithms show weaknesses to detect and control congestion due to the complexity and dynamics of the networks. Hence, this paper proposes a new AQM algorithm based on model predictive control (MPC) theory which has been widely applied in nonlinear and time-delay systems. In order to adjust the parameters of the MPC-based AQM algorithm adaptively according to network scenario variations, the adaptive mechanism is introduced into the new algorithm, named PHAQM, by using the Hebb learning rules from the neural network control theory. The simulation results show that the algorithm is effective in avoiding network congestion. Compared to the traditional AQM schemes, such as PI, REM, and GPC algorithm, the PHAQM has a faster convergence rate and smaller queue length fluctuations and outperforms especially under dynamically changing network situations.

A neuron-based active queue management scheme for internet congestion control

A neuron-based active queue management scheme for internet congestion control

A neuron-based active queue management scheme for internet congestion control

To deal with nonlinear and complex problems of internet congestion control, an intelligent scheme is required, which can learn the traffic pattern of the network. In this paper, we design a robust AQM scheme called neuron-based AQM (N-AQM) to efficiently control the complex network congestion problem and achieve QoS. In N-AQM, a neural network is used to predict the future value of current queue length and estimate the differential queue length error and use it to define the packet drop probability. Our simulation result demonstrates that N-AQM is stable, robust and outperforms other AQM schemes. From the result section, it is observed that N-AQM is more efficient in stabilising the queue length around the target with faster settling time and incurs lower oscillation than others.