Stabilizing Average Queue Length in Active Queue Management Method

The developments in computer networks in recent days such as the internet have increased rapidly. Connections of these networks necessitate resources in order to send their data to their prospective destinations. Further, the connections require high speed router buffers which they route data in high speed. Congestion is one of the main issues that occur at the router buffer cause deterioration of the network performance, i.e. increasing average waiting time, decreasing throughput, etc. Gentle Random Early Detection (GRED) is one of the known congestion control algorithms proposed to detect congestion before the router buffer overflows. In fact, GRED improves the setting of the parameters for the maximum threshold position (max threshold) at the router buffer and the maximum value for the packet dropping probability (D max ). This paper proposes an Adaptive GRED algorithm that detects congestion at router buffers in an preliminary stage, and enhances the parameters setting of the max threshold and the D max . During congestion, the simulation results reveal that the Adaptive GRED drops fewer packets than GRED, and it marginally offers better performance results than that of GRED.

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.

Transmission Control Protocol (TCP) used multipath for achievement of data instantaneously to improve its performance.Anyhow, the preceding TCP protocol in the Internet of Things (IoT) network experienced a struggle to transmit a superior number of subflows.The Bottleneck Bandwidth and Round-trip propagation time (BBR) generate a network path model by assessing the available bottleneck bandwidth and the minimal Round-Trip Time (RTT) to achieve the desired delivery rate, consequently reducing the latency.However, some research studies have indicated the presence of significant fairness issues related to RTT in the BBR algorithm.To overcome this issue, we proposed a New Enhanced Congestion Control (CC) mechanism referred to as Bottleneck Bandwidth and Round-trip propagation time (BBR-NEnh) which is cross-layer-based efficient congestion control in a 5G IoT heterogeneous network.The proposed work encloses three sequential processes including grid-based network construction, improved two-factor clustering, and Hybrid TCP congestion control.Initially, we perform network construction by grid-based to improve the information transfer rate and increase connectivity among devices.After that, IoT devices are clustered based on two factors congestion and buffer space using the Improved K-Medoids algorithm (IK-Med) by the edge server.Further, to enhance the efficiency of TCP, the proposed research adopts a BBR-NEnh by optimizing the congestion control parameters using the Adaptive Tunicate Swarm Optimization (ATSO) algorithm.Finally, the dynamic handover and adjustment of windows are performed based on the Fast Adaptation Technique (FAdT) which can send the data quickly without congestion.The implementation of the proposed research is carried out using NS-3.26 and the performance of the proposed BBR-NEnh model is evaluated using a variety of performance metrics, including goodput, delay, packet loss, queue length, transmission rate, and throughput.The performance of the proposed work is compared with IMPRTT (Improved RTT) and CW-IoT (Congestion Window Algorithm for the Internet of Things).The average throughput, goodput, and packet loss of the proposed work for 1500 packets size are 4, 0.9, and 10.3, and the average delay, transmission rate, and queue length over 150 seconds are 0.004, 2500, and 36.1, demonstrating that the proposed work outperforms earlier work like IMPRTT and CW-IoT.

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

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.

The objective of congestion control is to maximize network bandwidth utilization and minimize the average flow completion time in data center networks. Key performance indicators required to achieve this objective are high throughput and low packet latency. Existing approaches have proposed various methods to control packet delivery based on various network events or parameters such as packet loss, bottleneck bandwidth, RTT, and queue length. However, these methods often result in suboptimal packet transmission states that compromise throughput or latency. We provide a method to overcome this drawback. To this end, we introduce a congestion control method called PRTT (Predicted RTT) that leverages predicted RTT for congestion control. By using accurately predicted RTT values, PRTT dynamically adjusts the packet delivery rate to control the number of in-flight packets. This enables the transmission to approach states where the buffer holds only a few packets while fully utilizing the link bandwidth. Experimental results show that PRTT achieves higher throughput, lower latency, and shorter flow completion times than other state-of-the-art methods, particularly under bursty traffic. These results demonstrate that PRTT offers a promising solution for congestion control.

Due to the relatively high node density and source-to-sink communication pattern, wireless sensor networks (WSNs) are subject to congestion and packet losses. Further, the availability of low-cost hardware, such as Cyclops cameras, is promoting wireless multimedia sensing to support, for example, visual surveillance. As a result, congestion control is becoming more critical in WSNs. In this paper, we present a lightweight distributed congestion control method in WSNs. We develop new metrics to detect congestion in each node by considering the queue lengths and channel conditions observed in the one-hop neighborhood. Based on the estimated level of congestion, each node dynamically adapts its packet transmission rate and balance the load among the one-hop neighbors to avoid creating congestion and bottleneck nodes. In a simulation study performed in OMNeT++, our approach significantly enhances the end-to-end (e2e) packet delivery ratio and reduces the e2e delay without increasing the total energy consumption compared to the tested baseline approach.

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.

A large number of congestion control protocols have been proposed in the last few years, with all having the same purpose-to divide available bandwidth resources among different flows in a fair manner. Each protocol operates on the paradigm of some conception of link price (such as packet losses or packet delays) that determines source transmission rates. Recent work on network utility maximization has brought forth idea that the fundamental price or Lagrange multiplier for a link is proportional the queue length at that link, and that different congestion metrics (such as delays or drops) are essentially ways of interpreting such a Lagrange multiplier. We thus ask the following question: Suppose that each flow has a number of congestion control protocols to choose from, which one (or combination) should it choose? We introduce a framework wherein each flow has a utility that depends on throughput, and also has a disutility that is some function of the queue lengths encountered along the route taken. Flows must choose a combination of protocols that would maximize their payoffs. We study both the socially optimal, as well as the selfish cases to determine the loss of system-wide value incurred through selfish decision making, so characterizing the "price of heterogeneity". We also propose tolling schemes that incentivize flows to choose one of several different virtual networks catering to particular needs, and show that the total system value is greater, hence making a case for the adoption of such virtual networks.

In the conventional router architecture of Network-on-Chips (NoCs), each input port employs a set of dedicated flit buffers to store incoming flits. This mechanism unevenly distributes flits among router buffers, which in turn leads to higher packet blocking rates and under-utilization of buffers. In this paper, we address this problem by proposing two novel buffering mechanisms and their corresponding architectures to share flit buffers among several ports of a router efficiently. Our first proposed mechanism is called Minimum-First buffering. This mechanism distributes flits among buffers of input ports based on the number of free buffer slots available in each port, giving priority to minimum occupied buffers. This approach increases the utilization of underutilized buffers by allowing them to store flits of other input ports. The second mechanism (so-called Inverse-Priority buffering) is a lighter yet efficient, flexible buffering technique. This mechanism employs a simple priority order for each buffer. According to our analysis, prioritizing specific ports over others balances the traffic loads between router buffers, and thus yields higher throughput. Both mechanisms lead to lower waiting times in the router and higher utilization in hardware resources. After studying all possible scenarios and analyzing corner cases, we have optimally designed two router architectures equipped with the proposed buffering mechanisms. Moreover, a hardware optimization technique is introduced to reduce the area overhead of the Minimum-First router architecture. The proposed architectures show significant improvements in the performance of 3D-NoCs in terms of the average network throughput and average delay as well as the total number of blocked packets compared to different state-of-the-art and baseline router architectures.

Congestion control and reduction is paramount in enhancing the performance and speed of communication networks. Despite its efficiency in improving the speed and performance of communication networks, it is imperative to mention that the technique exhibit several issues, such as scheduling algorithms. This paper presents a comparison of different results associated with different scheduling algorithms. Multisource was used to simulate and test different scheduling algorithms, including First In First Out (FIFO), Random Packet Drop, and Last In First Out (LIFO). The comparison was made by analyzing different results, including the average delay vs arrival rate, average buffer utilization vs arrival rate, and packet loss ration vs arrival rate. According to the results, it is evident that the average buffer utilization and average packet delays increased as the rate of data transmitted through the network increased. There was no significant change noticed in the average buffer utilization in all the three algorithms. However, the packet loss ratio was high in Random Packet Loss algorithm than in both FIFO and LIFO for slower arrival rates below 1000. Similarly, FIFO exhibited a significantly high average packet delay than both LIFO and Random Packet Drop algorithms.

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.

A network is very important for fast communication in real life but it is suffered from many problems and one of them is packets loss due to congestion. Congestion Control helps to prevent internet from gridlock state. When a packets switch becomes congested, its buffer can overflow and packets loss can occur. The internet avoids this problem by forcing end system to decrease the rate of sending packets into the network during congestion period. The end system uses algorithm to control the congestion is called Congestion Control (CC) algorithm. The CC algorithm has three major components additive-increase, multiplicative-decrease and slow-start. Accumulation-based congestion control (ACC) has two mechanism, first is TCP Vegas which is used to control the congestion by estimating the accumulation at sender end. Another is, Monaco scheme that solve problems of TCP Vegas by employing an out-of-band, receiver-based accumulation estimator, with the support of two FIFO priority queues. These schemes suffer from router buffer scalability problem which is solved by the use of Active Queue Management (AQM) .This paper illustrates the AQM scheme on the basis of throughput, fairness, and scalability and these parameters are analyzed by Network Simulator (NS-2) using dumb-bell topology, single bottleneck.

First Person Shooters are a genre of online games in which users demand a high interactivity, because the actions and the movements are very fast. They usually generate high rates of small packets which have to be delivered to the server within a deadline. When the traffic of a number of players shares the same link, these flows can be aggregated in order to save bandwidth. Certain multiplexing techniques are able to merge a number of packets, in a similar way to voice trunking, creating a bundle which is transmitted using a tunnel. In addition, the headers of the original packets can be compressed by means of standard algorithms. The characteristics of the buffers of the routers which deliver these bundled packets may have a strong influence on the network impairments (mainly delay, jitter and packet loss) which determine the quality of the game. A subjective quality estimator has been used in order to study the mutual influence of the buffer and multiplexing techniques. Taking into account that there exist buffers which size is measured in terms of bytes, and others measured in packets, both kinds of buffers have been tested, using different sizes. Traces from real game parties have been merged in order to obtain the traffic of 20 simultaneous players sharing the same Internet access. The delay and jitter produced by the buffer of the access router have been obtained using simulations. In general, the quality is expected to be reduced as the background traffic grows, but the results show an anomalous region in which the quality rises with the background traffic amount. Small buffers present better subjective quality results than bigger ones. When the total traffic amount gets above the available bandwidth, the buffers measured in bytes add to the packets a fixed delay, which grows with buffer size. They present a jitter peak when the offered traffic is roughly the link capacity. On the other hand, buffers which size is measured in packets add a smaller delay, but they increase packet loss for gaming traffic. The obtained results illustrate the need of knowing the characteristics of the buffer in order to make the correct decision about traffic multiplexing. As a conclusion, it would be interesting for game developers to identify the behaviour of the router buffer so as to adapt the traffic to it.

In Network-on-chip design two of the most important performance indices are the average delay of packets and power dissipation. The first depends on the level of congestion of the communication system, the second is strongly influenced by the power dissipated by the links of a network-on-chip (NoC) which accounts for a significant fraction of the overall power dissipated by the on-chip communication fabric. Such fraction becomes more and more relevant as technology shrinks. The selection policy of the output port in the NOC router with adaptive routing function affects both the performance and power dissipation. In fact, generally a NOC with a lower average delay has a lower power dissipation on the links for transmission. The selection policies proposed in the literature have the object or the minimization of the average delay or in some cases the reduction of power dissipation. In this paper we propose a selection policy aimed at minimizing both the average delay and power consumption of the communication system of NoC based architectures. The proposed policy uses a cost function obtained using a Fuzzy Rule Base System (FRBS) with two inputs an one output. Inputs are the level of use of the buffers of the downstream routers, and the link power dissipation of the selected output. The output of FRBS is the cost function. The experimental results, obtained using a cycle accurate NoC simulator for different traffic scenarios, shows that the proposed selection policy outperforms other selection approaches both in terms of average delay and in terms on power dissipation and energy consumption.