A Sketch-Based Fine-Grained Proportional Integral Queue Management Method

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

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

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.

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.

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.

In this paper, we propose a novel active queue management (AQM) algorithm called Neuron Control RED (NC-RED) that overcomes the drawbacks of the original RED. NC-RED uses a neuron controller to adaptively adjust the maximum drop probability to stabilize the average queue length around the target queue length. We demonstrate by simulations that NC-RED maintains stable operation independent of traffic loading, round trip propagation delay, and bottleneck capacity. We also demonstrate that NC-RED is robust to non-responsive UDP traffic and HTTP traffic, and it is effective for networks with multiple bottlenecks. Comparison with other well-known AQM algorithms like PI, REM and ARED demonstrates the superiority of NC-RED in achieving faster convergence to queue length target and smaller queue length jitter.

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.

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.

An Active Queue Management (AQM) is a proactive scheme that controls the network congestion by avoiding the congestion before it happened. When implementing AQM in wireless networks several contemporary issues must be considered, such as interference, collisions, multipath-fading, propagation distance, shadowing effects and route failure, and whether the wireless networks is WLAN or other type. Therefore, the needs for AQM algorithm that can perform in WLAN network as good and efficient as in wired network become so crucial. This paper proposes a new AQM algorithm called Hybrid Fair AQM (HF-AQM) that can achieve better fairness and higher utilization in WLAN environment by hybridizing queue delay and input rate to measure network congestion. The simulation results show that HF-AQM manages to gain a higher fairness level in comparison to several types of AQM schemes with higher link utilization due to the adaptation and hybridization that been used in its congestion indicator, control function and feedback function.

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.

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) 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.

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.

In 802.11 wireless local area networks, the wired and the wireless interfaces of the access point are characterized by the disparity in channel capacity. This presents a significant bottleneck for traffic flowing from the wired network to the wireless network. Therefore, the implementation of a congestion control mechanism in access points promises to be a suitable solution. Recently, a particle swarm optimized fuzzy logic congestion detection (FLCD) was proposed for IP networks. This algorithm synergistically combines the good characteristics of traditional active queue management (AQM) algorithms and fuzzy logic based AQM algorithms. Its membership functions are designed automatically by using a multi-objective particle swarm optimization (MOPSO) algorithm in order to achieve optimal performance on all the major performance metrics of IP congestion control. In this paper, we evaluate the performance of the FLCD algorithm in the wireless local area network environment. We compare its performance with that of ARED and the Droptail mechanism. Simulation results show that the FLCD algorithm helps to minimize UDP traffic delay, packet loss rates. In terms of throughput, the FLCD algorithm exhibits similar performance to the other schemes. Its UDP jitter is slightly higher than that of the Droptail mechanism but better than that of ARED.