Improving RED by a Neuron Controller

A fuzzy logic-based AQM for real-time traffic over internet

The phenomenon “bufferbloat” occurs when the buffers of the network intermediary nodes fill up, causing long queuing delays. This has a significant negative impact on the quality of service of network applications, particularly those that are sensitive to time delay. Many active queue management (AQM) algorithms have been proposed to overcome this problem. Those AQMs attempt to maintain minimal queuing delays and good throughput by purposefully dropping packets at network intermediary nodes. However, the existing AQM algorithms mostly drop packets randomly based on a certain metric such as queue length or queuing delay, which fails to achieve fine-grained differentiation of data streams. In this paper, we propose a fine-grained sketch-based proportional integral queue management algorithm S-PIE, which uses an additional measurement structure Sketch for packet frequency share judgment based on the existing PIE algorithm for the fine-grained differentiation between data streams and adjust the drop policy for a differentiated packet drop. Experimental results on the NS-3 simulation platform show that the S-PIE algorithm achieves lower average queue length and RTT and higher fairness than PIE, RED, and CoDel algorithms while maintaining a similar throughput performance, maintaining network availability and stability, and improving network quality of service.

Deploying the active queue management (AQM) algorithm on a router is an effective way to avoid packet loss caused by congestion. In an information-centric network (ICN), routers not only play a role of packets forwarding but are also content service providers. Congestion in ICN routers can be further summarized as the competition between the external forwarding traffic and the internal cache response traffic for limited bandwidth resources. This indicates that the traditional AQM needs to be redesigned to adapt to ICN. In this paper, we first demonstrated mathematically that allocating more bandwidth for the upstream forwarding flow could improve the quality of service (QoS) of the whole network. Secondly, we propose a novel AQM algorithm, YELLOW, which predicts the bandwidth competition event and adjusts the input rate of request and the marking probability adaptively. Afterwards, we model YELLOW through the totally asymmetric simple exclusion process (TASEP) and deduce the approximate solution of the existence condition for each stationary phase. Finally, we evaluated the performance of YELLOW by NS-3 simulator, and verified the accuracy of modeling results by Monte Carlo. The simulation results showed that the queue of YELLOW could converge to the expected value, and the significant gains of the router with low packet loss rate, robustness and high throughput.

Design and performance evaluation of a fuzzy based traffic conditioner for differentiated services

Active Queue Management (AQM) algorithms have seen a lot of attention in recent academic literature as well as in the popular press. The problem with traditional AQM is that it is designed to operate on queues at the IP layer. However, the problem popularly called bufferbloat can move about among many queues some of which are resistant to traditional AQM such as Layer 2MAC protocols used in cable/DSL links. We call this problem bufferbloat displacement. Our contribution is a new class of AQM algorithm called Active Sense Queue Management (ASQM). ASQM is an IP layer AQM protocol that has the added benefit of managing Layer 2 link delay, i.e. bufferbloat displacement. We provide testbed experiments comparing ASQM's bandwidth delay characteristics with traditional AQM algorithms demonstrating ASQM's ability to manage bufferbloat displacement where traditional AQM algorithms cannot.

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.

Active Queue Management (AQM) is recommended by Internet Engineering Task Force to mitigate the bufferbloat phenomenon in the Internet. In this paper, we show the results of comprehensive measurements carried out in our university network, in which a device with an AQM algorithm, designed and programmed for this purpose, was running. The implemented AQM algorithm was based on the dropping function, i.e. arriving packets were dropped randomly, with the probability being a function of the queue length. Several different dropping function forms, proposed in the networking literature, were used, in addition to the classic FIFO queue (no AQM). The experiment lasted over a month, during which the state of the network was measured and recorded several thousand times. This made the results independent of the natural fluctuations of the users’ behavior and the network load. Conclusions on the general performance improvement offered by the implemented AQM, as well as the differences in the performance between particular forms of the dropping function, were reached. Some of these conclusions differ from those drawn previously from simulations. This underlines the need for carrying measurements of new AQMs in real, operating networks, with complex, natural traffic.

Fixed buffer sizing in computer networks, especially the Internet, is a compromise between latency and bandwidth. A decision in favor of high bandwidth, implying larger buffers, subordinates the latency as a consequence of constantly filled buffers. This phenomenon is called Bufferbloat. Active Queue Management (AQM) algorithms such as CoDel or PIE, designed for the use on software based hosts, offer a flow agnostic remedy to Bufferbloat by controlling the queue filling and hence the latency through subtle packet drops.In previous work, we have shown that the data plane programming language P4 is powerful enough to implement the CoDel algorithm. While legacy software algorithms can be easily compiled onto almost any processing architecture, this is not generally true for AQM on programmable data plane hardware, i.e., programmable packet processors. In this work, we highlight corresponding challenges, demonstrate how to tackle them, and provide techniques enabling the implementation of such AQM algorithms on different high speed P4-programmable data plane hardware targets. In addition, we provide measurement results created on different P4-programmable data plane targets. The resulting latency measurements reveal the feasibility and the constraints to be considered to perform Active Queue Management within these devices. Finally, we release the source code and instructions to reproduce the results in this paper as open source to the research community.

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.

Based on the system of Internet congestion control, an adaptive network controller is established as active queue management (AQM) algorithm to adjust the congestion marked possibility at the links, whose parameters is auto-tuned by applying the minimization of an integral criterion for adapting the change of the network. By comparing with those existent AQM schemes in the simulations, the adaptive AQM algorithm has the better robustness for the loading perturbation.

There is the problem of absolute discrimination in current Active Queue Management (AQM) algorithms for Diff-Serv. The absolute discrimination usually results in dropping continuously for the connections which exceed Committed Information Rate (CIR), the throughput and link utilization of network are decreased. In this paper we have presented an AQM algorithm, which we call Weighted-queue- length RIO-C (WRIO). WRIO draws directly from the original RIO-C proposal. WRIO realizes relative discrimination of different drop precedence, and improves link utilization. We analyze the action and rationality of queue thresholds for RIO-C and WRIO; the simulations show that threshold settings of WRIO are more reasonable. WRIO can improve performance of network, especially for the networks with TCP traffics.

Active Queue Management (AQM) addresses the problem arising from using unnecessarily large, unmanaged buffers and thus it aims at improving network and application performance. AQM methods introduce different drop policies to proactively drop packets according to queue states and parameters. Though we are living in an AQM renaissance, comprehensive methodology and framework for performance evaluation under realistic traffic loads are still missing. With the advent of P4, description, validation and evaluation of AQM algorithms in a generic framework have become possible since the different drop policies applied by these methods can be implemented in ingress and/or egress control blocks of a P4 program. In this demo paper, we propose an AQM evaluation framework in which AQM algorithms described in P4 language can be executed and evaluated with a modified version of our DPDK-based P4 compiler called T4P4S in a testbed with realistic traffic mixes. The framework is demonstrated with some selected AQM algorithms in a network with a 5 Gbps bottleneck.

The variable structure control algorithm has been proposed for active queue management (AQM) in TCP networks. In this paper we have considered the saturation constrain, i.e. marking probability p(t) /spl isin/ [0,1], which was omitted by many papers. By introducing the sliding model plane, this AQM algorithm can maintain an input rate around the target rate, while attempting to regulate the queue length. In this paper, we analyze the existence of the sliding mode plane in a nonlinear model. We intend to implement this algorithm in a per-flow queuing framework and we also guarantee that this algorithm can make the system converge to max-min fairness. We show the effectiveness of our algorithm compared to widely used AQM scheme RED through ns-2 simulations.

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.

In this study, the disturbance and uncertainty on nonlinear and time varying systems as Active Queue Management (AQM) is analyzed. Many of AQM schemes have been proposed to regulate a queue size close to a reference level with the least variance. We apply a normal range of disturbances and uncertainty such as variable user numbers, variable link capacity, noise, and unresponsive flows, to the three AQM methods: Random Early Detection (RED), Proportional-Integral (PI) and Improved Neural Network (INN) AQM. Then we examine some important factors for TCP network congestion control such as queue size, drop probability, variance and throughput in NS-2 simulator, and then compare three AQM algorithms with these factors on congestion conditions. We present the performance of the INN controller in desired queue tracking and disturbance rejection is high.