Average Queue Size Research Articles

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.

AN \(M^{[X]}/G/1\) QUEUE WITH OPTIONAL SERVICE AND WORKING BREAKDOWN

In this study, a batch arrival single service queue with two stages of service (second stage is optional) and working breakdown is investigated. When the system is in operation, it may breakdown at any time. During breakdown period, instead of terminating the service totally, it continues at a slower rate. We find the time-dependent probability generating functions in terms of their Laplace transforms and derive explicitly the corresponding steady state results. Furthermore, numerous measures indicating system performances, such as the average queue size and the average queue waiting time, has been obtained. Some of the numerical results and graphical representations were also presented.

ABOUT THE AVERAGE MAXIMUM VALUES OF QUEUES IN QUEUING SYSTEMS

In the classical theory of queuing systems, the assumption of mutual independence of events is fundamental, so we propose to consider the basic principles of the interval method of traffic analysis, which allows us to replace analysis of time intervals between neighboring requests and time intervals for processing requests by analyzing one random variable, i. e. the number of requests received during successive intervals of time for processing each of requests. The concepts of mean maximum values of queues and limiting maximum values are introduced. It is shown that the limit maximum value in a queue will be the largest of the possible maximum values for a given total value of requests in queues during one service cycle. The mean maximum score is the average of the maximum queue values. To determine the characteristics of traffic, we use the well-known «Leaky_bucket» algorithm. The proposed traffic model is based on the total number of requests during their processing cycles. Experimental modeling of the proposed method showed that variance and correlation properties of a specified random variable for a given load completely characterize the average queue size in a queuing system.

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>

Effects on random early detection of the packet drop probability function with an adjustable nonlinearity

This paper proposed a packet drop probability function with an adjustable nonlinearity parameter in random early detection (RED) for active queue management of a router to control network congestion. We investigated the effect of nonlinearity on the average queue size, average throughput, average retransmission rate, average round-trip time, and fairness index for two widely used loss-based congestion control algorithms: Reno and CUBIC. Simulations were performed with Tail-Drop and the original RED to clarify the effect of nonlinearity under different traffic loads. The results showed that the RED with a nonlinear function did not aggravate the network performance statistics because achieved high throughput while maintaining a low-queuing delay, such as the original RED with a linear function under the extremely heavy traffic condition. Under the light and the heavy traffic conditions, increasing the bending degree of the nonlinear function accomplished high throughput by preventing excessive discarding of packets.

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-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.

Performance Analysis of a Cloud Computing System using Queuing Model with Correlated Task Reneging

Queuing theory has been extensively used in the modelling and performance analysis of cloud computing systems. The phenomenon of the task (or request) reneging, that is, the dropping of requests from the request queue often occur in cloud computing systems, and it is important to consider it when developing performance evaluations models for cloud computing infrastructures. Majority of studies in the performance evaluation of cloud computing data centres with the use of queuing theory do not consider the fact that the tasks could be removed from queue without being serviced. The removal of tasks from the queue could be due to the user impatience, execution deadline expiration, security reasons, or as an active queue management strategy. The reneging could be correlated in nature, that is, if a request is dropped (or reneged) at any time epoch, and then there is a probability that a request may or may not be dropped at the next time epoch. This kind of dropping (or reneging) of requests is referred to as correlated request reneging. In this paper we have modelled a cloud computing infrastructure with correlated request reneging using queuing theory. An M/M/1/N queuing model with correlated reneging has been used to study the performance analysis of the load balancing server of a cloud computing system. The steady-state as well as the transient performance analyses have been carried out. Important measures of performance like average queue size, average delay, probability of task blocking, and the probability of no waiting in the queue are studied. Finally, some comparisons are performed which describe the effect of correlated task reneging over simple exponential reneging.

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.

Active Queue Management in RED Considering Critical Point on Target Queue

The basic philosophy behind RED is to prevent congestion. When the average queue length exceeds the minimum threshold, packets are randomly dropped, or the explicit congestion notification bit is marked. Since network requirements differ significantly, it is not an optimal approach to establish RED parameters with constant value. There is a new algorithm we are proposing called Critical Point on Target Queue (AQM-RED-CPTQ), provide greater congestion management over the network while also preserving the value of RED. To overcome the problem in RED without changing queue weight parameter, we have proposed few models to control the congestion by introducing range parameter with probability and control mechanism which will belong between minimum and maximum threshold. The current queue size is controlled together with average queue size. A new range variable has been introduced to improve the performance of priority queue of existing RED based algorithm which improves the overall performance of networks. For each packet, minimum and maximum threshold has been updated and dropped with probability (Pa) for a special condition. Instead of multiplicative increase and decrease the maximum probability, the scheme uses additive-increase and multiplicative-decrease. Once the AVG queue length is close to the minimum threshold value, our approach automatically sets queue parameter according to queue conditions and handles queuing delay and improve throughput. The simulated results proof that our approaches are better than RED in terms of throughput, end to end delay, packet delivery ratio and goodput.

Internet congestion control: From stochastic to dynamical models

Since its inception, control of data congestion on the Internet has been based on stochastic models. One of the first such models was Random Early Detection. Later, this model was reformulated as a dynamical system, with the average queue sizes at a router’s buffer being the states. Recently, the dynamical model has been generalized to improve global stability. In this paper we review the original stochastic model and both nonlinear models of Random Early Detection with a two-fold objective: (i) illustrate how a random model can be “smoothed out” to a deterministic one through data aggregation and (ii) how this translation can shed light into complex processes such as the Internet data traffic. Furthermore, this paper contains new materials concerning the occurrence of chaos, bifurcation diagrams, Lyapunov exponents and global stability robustness with respect to control parameters. The results reviewed and reported here are expected to help design an active queue management algorithm in real conditions, that is, when system parameters such as the number of users and the round-trip time of the data packets change over time. The topic also illustrates the much-needed synergy of a theoretical approach, practical intuition and numerical simulations in engineering.

Adaptive Queue Management and Traffic Class Priority Based Fairness Rate Control in Wireless Sensor Networks

Congestion control in Wireless Sensor Networks (WSNs) is one of the key areas of research and different algorithms have been proposed using either of the notions of fair rate allocation, traffic class priority, and queue management. Use of the any one of the above is not adequate to address the challenges. Hence, in this paper, we have proposed a novel congestion control algorithm using the combined notions of fair allocation of bandwidth, prioritizing traffic classes, and Adaptive Queue Management (ADQM). The proposed Weighted Priority based Fair Queue Gradient Rate Control (WPFQGRC) scheme achieves the fair distribution of spare bandwidth by considering the traffic class priority, average queue size, and the connected loads of a node. Average queue size at every node is adapted based on the proposed notion of the gradient of the differential of Global Priority (GP) with respect to the differential of queue size. The output rate of a given node is computed based on the GP of the node and the average queue size. The spare bandwidth of a node is fairly distributed by taking into account the connected load of the given node. The proposed algorithm is developed to suit to a general topology of WSN, however for the sake of illustration, we have considered a tree topology network that deals with both Real-Time (RT) and Non-Real Time (NRT) traffic classes. The proposed algorithm is implemented in NS3 platform in Linux environment and the performance of the algorithm is evaluated in terms of throughput, packet loss, packet delay, traffic class patterns, node mobility, and the average queue size. The performance of the proposed algorithm is found to be superior to that of Yaghmaee et al. ’s, Brahma et al. ’s, Monowar et al. ’s, Sarode et al. ’s, Difference of Differentials Rate Control (DDRC), Weighted Priority based DDRC (WPDDRC), and Priority based Fairness Rate Control (PFRC) algorithms respectively.

New View on Adversarial Queueing on MAC

This work proposes an average-case performance measurement as a meaningful and non-trivial way of evaluating and comparing queueing protocols on a multiple-access channel (MAC). We prove that Little's Law holds for this way of measuring latency and number of packets in the system, also for adversarial packet arrival (which was not the case for the previously considered worst-case measurement), and analyze performance of popular MAC protocols. Interestingly, some of them have asymptotically the same average and worst case queue sizes, but not packet latencies, while others exhibit entirely opposite behavior. We also propose a consistent methodology for classifying queueing protocols, based on protocol features, such as synchronization, collision detection, queue-size awareness etc.

The Evaluation QS-WFQ Scheduling Algorithm For IoT Transmission To Cloud

This study using the Weighted Fair Queue scheduling algorithm when the weights can change and calculated based on changes in the average queue size in the buffer. This algorithm divides the priorities of each sensor into three priorities, namely high, medium and low priority. Each queue is given a weight that is adjusted to the resource requirements of each traffic. High priority data will take precedence, but medium and low priority data will remain underserved and guaranteed by network resources.The results of this study show packet loss ratio when the ratio of the number of buffers and the amount of data is 1: 3 with variations in the number of high, medium and low priority buffers 75: 75: 150 and 50: 50: 200 is 0%. The delay time in the high priority and the medium priority buffer has almost the same delay time when data is transmitted, whereas for the low priority buffer increased in the delay time.

Recycling Cellular Energy for Self-Sustainable IoT Networks: A Spatiotemporal Study

This paper investigates the self-sustainability of an overlay Internet of Things (IoT) network that relies on harvesting energy from a downlink cellular network. Using stochastic geometry and queueing theory, we develop a spatiotemporal model to derive the steady state distribution of the number of packets in the buffers and energy levels in the batteries of IoT devices given that the IoT and cellular communications are allocated disjoint spectrum. Particularly, each IoT device is modelled via a two-dimensional discrete-time Markov Chain (DTMC) that jointly tracks the evolution of the data buffer and energy battery. In this context, stochastic geometry is used to derive the energy generation at the batteries and the packet transmission success probability from buffers taking into account the mutual interference from other active IoT devices. To this end, we show the Pareto-Frontiers of the sustainability region, which define the network parameters that ensure stable network operation and finite packet delay. Furthermore, the spatially averaged network performance, in terms of transmission success probability, average queueing delay, and average queue size are investigated. For self-sustainable networks, the results quantify the required buffer size and packet delay, which are crucial for the design of IoT devices and time critical IoT applications.

Generalized TCP-RED dynamical model for Internet congestion control

Abstract Adaptive management of traffic congestion in the Internet is a complex problem that can gain useful insights from a dynamical approach. In this paper we propose and analyze a one-dimensional, discrete-time nonlinear model for Internet congestion control at the routers. Specifically, the states correspond to the average queue sizes of the incoming data packets and the dynamical core consists of a monotone or unimodal mapping with a unique fixed point. This model generalizes a previous one in that additional control parameters are introduced via the data packet drop probability with the objective of enhancing stability. To make the analysis more challenging, the original model was shown to exhibit the usual features of low-dimensional chaos with respect to several system and control parameters, e.g., positive Lyapunov exponents and Feigenbaum-like bifurcation diagrams. We concentrate first on the theoretical aspects that may promote the unique stationary state of the system to a global attractor, which in our case amounts to global stability. In a second step, those theoretical results are translated into stability domains for robust setting of the new control parameters in practical applications. Numerical simulations confirm that the new parameters make it possible to extend the stability domains, in comparison with previous results. Therefore, the present work may lead to an adaptive congestion control algorithm with a more stable performance than other algorithms currently in use.

HRED, An Active Queue Management Algorithm for TCP Congestion Control

Background: Active Queue Management (AQM) is a TCP congestion avoidance approach that predicts congestion before sources overwhelm the buffers of routers. Random Early Detection (RED) is an AQM strategy that keeps history of queue dynamics by estimating an average queue size parameter avg and drops packets when this average exceeds preset thresholds. The parameter configuration in RED is problematic and the performance of the whole network could be reduced due to wrong setup of these parameters. Drop probability is another parameter calculated by RED to tune the drop rate with the aggressiveness of arriving packets. Objective: In this article, we propose an enhancement to the drop probability calculation to increase the performance of RED. Methods: This article studies the drop rate when the average queue size is at the midpoint between the minimum and maximum thresholds. The proposal suggests a nonlinear adjustment for the drop rate in this area. Hence, we call this strategy as the Half-Way RED (HRED). Results: Our strategy is tested using the NS2 simulator and compared with some queue management strategies including RED, TD and Gentle-RED. The calculated parameters are: throughput, link utilization and packet drop rate. Conclusion: Each performance parameter has been plotted in a separate figure; then the robustness of each strategy has been evaluated against these parameters. The results suggest that this function has enhanced the performance of RED-like strategies in controlling congestion. HRED has outperformed the strategies included in this article in terms of throughput, link utilization and packet loss rate.

Simulation Modeling and Performance Comparison of RED and ERED Algorithms using Congestion Indicators

Random early detection (RED) is an effective congestion control mechanism acting on the intermediate gateways. The gateway detects and controlling packet dropping function with average queue size. The gateway could notify contacts of congestion either by simply dropping packets arriving in the gateway or simply by setting a bit within packet headers. When the average queue size surpasses a preset threshold, the gateway drops or represents each arriving packet along with a certain probability, where exact probability is a function of the average queue size. In this study describe a new active queue management scheme, Effective RED (ERED) that aims to reduce packet loss rates in a simple and scalable manner. I made a few changes to the packet drop function of the existing RED scheme. The rest of the original RED remains unchanged. In this study, the Simulations demonstrate that ERED achieves a highest throughput and lowest packet drops than RED. ERED is fully compatible with RED, can easily upgrade/replace the existing RED implementations by ERED.

Queue stability and low energy for energy harvesting cognitive radio networks

Cognitive radio network explores the framework for energy harvesting and derives effective algorithms such as Nash Equilibrium re-Optimization, Greedy one-slot Game, and Bi Layer re Game. The stability of data queue for energy harvesting cognitive radio network was analyzed by optimization of primary users and noncooperative game of secondary users with Nash equilibrium of stochastic Stackelberg game in a bilevel manner. The proposed work considers the effective data queue stability for secondary user in a cognitive radio network with the necessary conditions such as data arrival, energy arrival, transmission success probability, and expected transmission steady-state delay to make the data queue stable. The queue needs to be stabilized for online algorithm Greedy one-slot Game and offline algorithm Bi Layer re Game. The simulations show the relevance of queue stability by finding the average queue delay and average queue size by comparing both the algorithms.