Perceptual quality-aware active queue management for video transmission

Ad hoc networking uses wireless technologies to construct networks with no physical infrastructure and so are expected to provide instant networking in areas such as disaster recovery sites and inter-vehicle communication. Unlike conventional wired networks services, services in ad hoc networks are easily disrupted by the frequent changes in traffic and topology. Therefore, solutions to assure the Quality of Services (QoS) in ad hoc networks are different from the conventional ones used in wired networks. In this paper, we propose a new queue management scheme, Interference Drop Scheme (IDS) for ad hoc networks. In the conventional queue management approaches such as FIFO (First-in First-out) and RED (Random Early Detection), a queue is usually managed by a queue length limit. FIFO discards packets according to the queue limit, and RED discards packets in an early and random fashion. IDS, on the other hand, manages the queue according to wireless interference time, which increases as the number of contentions in the MAC layer increases. When there are many MAC contentions, IDS discards TCP data packets. By observing the interference time and discarding TCP data packets, our simulation results show that IDS improves TCP performance and reduces QoS violations in UDP in ad hoc networks with chain, grid, and random topologies. Our simulation results also demonstrate that wireless interference time is a better metric than queue length limit for queue management in multi-hop ad hoc networks.

We present a virtual queue-based active queue management (AQM) scheme, called stabilized virtual buffer (SVB). The SVB scheme uses the packet arrival rate and queue length information to drop/mark packets probabilistically in a congested Internet router. System goodput, packet loss rate, average queue length, and stability of the queue are used to compare the proposed SVB scheme with prior AQM schemes (RED - random early detection; REM - random exponential marking; AVQ - adaptive virtual queue). Simulation results indicate that the SVB algorithm can provide better goodput and lower loss rate than the other three AQMs. The most striking feature of the proposed scheme is its robustness to workload fluctuations in maintaining a stable queue for different workload mixes (short and long flows) and parameter settings.

Schemes described in the literature on network congestion management are in general based on queue management. It is widely accepted that Poisson model is not sufficient to characterize the traffic in current Internet. In this paper, we present an alternate RED (Random Early Detection) algorithm for traffic congestion management in IP networks having self-similar (SS) input. We first discuss the basic scheme of Normal and Gentle RED as proposed by Floyd et.al., for the Poisson input model, and then explain piecewise RED, an extension of Gentle RED and Exponential RED - a new AQM proposed by us. We then explain the modification to these algorithms that we propose for the self-similar IP traffic input. Our modification takes into consideration probability values corresponding to the average queue lengths for computing the marking/dropping probability. Verification of the algorithms proposed viz., Normal SS RED and Exponential SS RED, vis-a-vis that of Floyd has been done using simulated self-similar traffic. Results of the verification have been discussed in the paper.

In data communication when there are too many packets contending for the limited shared resources, such as the queue buffer in the router at bottleneck link, it may result in congestion. During congestion, large amount of packets experience delay or even be dropped due to the queue overflow. Severe congestion problems result in degradation of the throughput and large packet loss rate. In this paper, analysis of throughput and packet loss rate for different buffer sizes is presented; taking into account two popular queue management methods, Random Early Detection (RED) and Drop Tail, for different TCP and UDP streams coming to a common router buffer. The results indicate that the throughput and efficiency of TCP increases with increase in buffer size for RED. Moreover, UDP packet loss decreases with an increase in buffer size while the buffer size has no key impact on UDP packet delays. Simulation has been done by using Network Simulator (NS2-2.37) and the graphs are plotted using GNU plot.

When the number of packets increases beyond the network capacity leads to congestion in the computer network. Congestion effects the QOS parameters severely by decreasing throughput and increasing packet loss and delay, thus degrading the overall network performance. Active queue management algorithm is router based mechanism for early detection of congestion in the computer network. The basic idea of AQM is to sense and detect congestion in advance and to inform the sender to reduce its sending rate there by reducing the number of packets sent in the network to control the congestion in the CN. AQM algorithm is congestion control algorithm which increases throughput, link utilization and decreases the delay and packet loss. RED (Random Early Detection) provides the mechanism for congestion avoidance. The technique has several drawbacks and one among them is inability to deal with busty traffic. The major drawback of Blue algorithm is that it detect the congestion by analyzing and observing the queue length, queue length may little information about the severity of the congestion and detection of number of competing connections. The proposed algorithm called AdvancedBlue which is the modification of Blue AQM algorithm uses idle link, packet loss to detect congestion decouples the congestion measure with performance measure and stabilizes the performance measure against the target independently of number of users. AdvancedBlue AQM is simulated in wireless network using NS2 simulator. Wireless network is quite different from the wired network. The nodes have no wired links to other nodes and base station. So any barrier that is between the sender and the receiver could influence the transmission power and bring packet loss. The proposed AQM showed a better performance in wired network and was more complicated in wireless network. The proposed algorithm when compared with existing DropTail algorithm in wireless network showed better performance with respect to throughput and delay but had more packet loss. Keywords: AQM, AdvancedBlue, Blue, CN, DropTail, NS2, QOS, RED Cite this Article: Uma R. Pujeri. Advanced AQM Blue Algorithm in Wireless Network. International Journal of Wireless Network Security. 2020; 6(1): 16–24p.

The purpose of this research is to design and evaluate a new queue management algorithm effectively controlling the heavy-tailed traffics. Among active queue managements (AQM), Random Early Detection (RED), has been widely used to improve the TCP throughput. The heavy-tailed traffic, however, generates the large fluctuation on the queue causing the degradation of fairness in terms of jitter performance. In this paper, we proposed the Stable time RED (STRED) to improve the jitter and the fairness of jitter performance. STRED enables to stabilize the calculation timing for average queue length leading to suppress the queue fluctuations. We also implemented the STRED mechanism on the ns-2 simulator. In addition, we propose the pseudo model network consisting of multiple input / output buffers. The experiments of different pattern of AQM algorithms were conducted. As the results of simulation, we extracted the following features. Firstly, STRED could sustain the jitter values and large fluctuations of each file size and Pareto distribution. Secondly, STRED at the first incoming buffer made lower and smoother jitter values than the RED method. Thirdly, STRED could improve the fairness of jitter for all file sizes, and especially for heavy-tailed traffics. Finally, STRED creates low jitters and the high fairness in term of jitter meaning that STRED can enhance the quality of mutual communication applications such as Internet telephony.

Internet is the major source of information today and its usage is increasing at an alarming rate. A wide variety of data travels over the Internet to cater the needs of end users. This has eventually led to heavy congestion in the network which in turn, worsens the user perceived latency. Internet routers are the main agents that detect congestion prior to end hosts. Traditional router incorporates Passive Queue Management (PQM) strategies which fail to control congestion. Moreover, PQM has several drawbacks which drew the attention of researchers towards the evolution of Active Queue Management (AQM). AQMs are designed to effectively avoid congestion at network routers. AQM apparently became very popular for wired networks, but there are very few researches to find their effectiveness over wireless networks. In this paper we evaluate the effectiveness of a recently proposed AQM mechanism called Controlled Delay (CoDel) in wired-cum-wireless networks. Simulations are carried out by using ns-2 and CoDel's performance is compared with that of Random Early Detection (RED) and Droptail.

Explicit congestion notification (ECN) and active queue management (AQM) Schemes such as random early detection (RED), adaptive random early detection (ARED) and BLUE queues have been proposed for TCP/IP networks to compensate network congestion. However, using ECN requires that ECN be supported by both TCP senders and receivers. This paper presents a novel AQM modification called receiver- window modification (RWM). RWM can be used together with RED, ARED and BLUE queues, to provide congestion avoidance in packet switched networks at ingress and gateway routers. RWM does not require modification to all end system TCP/IP stacks but can be solely implemented in routers. Our RWM scheme helps in reducing the average queue sizes of RED, ARED, BLUE and even ARED-ECN, BLUE-ECN and RED- ECN queues. By reducing the average queue sizes, RWM queues reduce the queuing delay resulting in significant improvements in one-way end-to-end packet delays and dropped packets. It is also shown that the performance of RED- ECN, ARED-ECN and BLUE-ECN queues is heavily dependent on the queues of the downstream routers. RWM modified queues in ingress or gateway routers are not influenced by the number and state of the downstream router as they will piggyback congestion information to the source in the next available acknowledgement packet. We carry out extensive ns2 simulations to show our results and to support our claims.

Active Queue Management (AQM) is an active research area in the Internet community. Random Early Detection (RED) is a typical AQM algorithm, but it is known that it is difficult to configure its parameters and its average queue length is closely related to the load level. This paper proposes an effective fuzzy congestion control algorithm based on fuzzy logic which uses the predominance of fuzzy logic to deal with uncertain events. The main advantage of this new congestion control algorithm is that it discards the packet dropping mechanism of RED, and calculates packet loss according to a preconfigured fuzzy logic by using the queue length and the buffer usage ratio. Theoretical analysis and Network Simulator (NS) simulation results show that the proposed algorithm achieves more throughput and more stable queue length than traditional schemes. It really improves a router’s ability in network congestion control in IP network.

Quality of Service (QoS) and queue management are important issues for IEEE 802.11 systems. However, existing 2-dimensional (2-D) Markov chain models of 802.11 systems are unable to capture the complete QoS performance and queueing behavior due to the lack of an adequate finite buffer model. We present a 3-dimensional (3-D) Markov chain that integrates the 802.11 system contention resolution and queueing processes into one model. The 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> dimension, that models the queue length, allows us to accurately capture important QoS measures, delay and loss, plus throughput and queue length, for realistic 802.11 systems with finite buffer under finite load. We derive an efficient method for solving the steady state probabilities of the Markov chain. Our 3-D Markov chain is the first finite buffer model defined and solved for 802.11 systems. The solutions, validated by extensive simulations, capture the system dynamics over a wide range of traffic load, buffer capacity, and network size. Our 3-D model points to the existence of an effective maximum throughput and shows its relationship with buffer capacity. We demonstrate that our 3-D model can also be used in resource allocation to determine adequate buffer sizes under a particular QoS constraint.

This paper presents a comparison of end-to-end recovery and control methods for the ITU-T G.729 CS-ACELP codec operating at 8 Kbps. We have tested Media independent forward error correction (FEC) parity 3, Media independent FEC parity 4 and Media specific FEC combined with queue management methods namely Drop Tail, Predictive Loss Pattern (PLoP) and Random Early Detection (RED). The performance measures using perceptual evaluation of speech quality (PESQ) tests under various packet loss conditions prove that Media independent FEC parity 3 combined with RED algorithm give better results.

This paper proposes a router congestion control mechanism called PRED (Prediction-enabled RED), a more adaptive and proactive version of RED (Random Early Detection). In essence, PRED predicts its queue length for an early detection of possible congestion alerts in the near future and operates adaptively to the predicted changes in traffic patterns. Typically, PRED does this by first making prediction about average queue length and then using the predicted average queue length to adjust three classic RED parameters max th, min th, and max p. The incoming packets after the adjustment are now being dropped with the new probability defined by updated parameters. Due to its adaptability and proactive reaction to network traffic changes, PRED can be considered as a novel solution to dynamically configure RED. Extensive simulation results from NS-2 simulator are presented to verify the performance and characteristics of PRED.

The rapid growth of the food and beverage sector, especially in bustling urban environments, requires ongoing improvements in operational effectiveness to keep up with the rising demand and client standards. This study examines the queue management system at the Hasanudin branch of Bebeke Om Aris, a renowned restaurant in Bandung, Indonesia, that specializes in duck and chicken cuisine. The restaurant's operational performance is significantly impacted by extended waiting times experienced during busy periods. The objective of this research is to to develop and evaluate strategies for diminishing waiting times and enhancing overall efficiency through a quantitative methodology. This study was driven by the observed inefficiencies in the current queue management system, characterized by huge waits and longer waiting periods, especially on weekends. These inefficiencies not only impede the delivery of services but also provide a possible threat to the restaurant's competitive standing in the market. The main goal of this study is to determine and recommend effective strategies for optimizing queue management in order to achieve a maximum waiting time of 5 minutes during the busiest hours, as specified by the restaurant's management. During a four-week period, a structured observation method was used to collect primary data on customer arrivals, queue lengths, and waiting times. The observation notably targeted Saturday evenings, which are known for having the highest client inflow. The gathered data was subsequently employed to generate simulation models using Arena software, which assessed different scenarios, such as the integration of self-service kiosks, the adoption of QR code ordering systems, and the augmentation of cashier counters. The queuing theory was employed to examine these scenarios and assess important performance measures, including average waiting time, queue length, and system utilization rates. The investigation demonstrated that the integration of self-service kiosks and QR code ordering systems can effectively decrease waiting times and enhance operational efficiency. The self-service kiosks enable customers to autonomously place orders, thereby decreasing the burden on cashiers and expediting the ordering procedure. Similarly, the implementation of the QR code system simplifies the procedure by allowing customers to place orders and make payments directly from their smartphones, thereby reducing congestion at the cashier. The simulation results demonstrated a significant reduction in both mean waiting times and queue lengths, thereby successfully attaining the intended performance goals. This study's findings enhance the subject of operational management by showcasing how queuing theory and simulation tools may be practically applied to optimize service delivery in the food service industry. The suggested solutions give a scalable approach that can be customized for other comparable environments, providing a foundation for restaurants seeking to improve their service efficiency and customer satisfaction. Subsequent studies could investigate the enduring impacts of these interventions on customer behavior and business performance, as well as their suitability in various categories of food service enterprises

Random Early Detection (RED) is one of the most commonly used Active Queue Management (AQM) algorithms that is recommended by IETF for deployment in the network. Although RED provides low average queuing delay and high throughput at the same time, but effectiveness of RED is highly sensitive to the RED parameters setting. As network condition varies largely, setting RED's parameters with fixed values is not an efficient solution. We propose a new method to dynamically tuning RED's parameters. For this purpose, we compute the rate of which the queue is occupied and consider it as a congestion metric that will be forecasted when the queue is overloaded. This meter is used to dynamically setting RED parameters. The simulation results show the effectiveness of the proposed method. According to the results, we achieve a significantly higher utilization and less packet loss comparing to original RED algorithm in dynamic conditions of the network.

Random early detection (RED) is an active queue management (AQM) mechanism for routers on the Internet. In this paper, performance of RED and adaptive RED is compared from the viewpoint of nonlinear dynamics. In particular, we reveal the relationship between the performance of the network and its nonlinear dynamical behavior. We measure the maximal Lyapunov exponent and Hurst parameter of the average queue length of RED and adaptive RED, as well as the throughput and packet loss rate of the aggregate traffic on the bottleneck link. Our simulation scenarios include FTP flows and Web flows, one-way and two-way traffic. In most situations, adaptive RED has smaller maximal Lyapunov exponents, lower Hurst parameters, higher throughput and lower packet loss rate than that of RED. This confirms that adaptive RED has better performance than RED.