QBLUE: A New Congestion Control Algorithm Based on Queuing Theory

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.

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

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.

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.

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.

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.

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.

Abstract—This work proposes a novel congestion management protocol for constrained delay traffic and optimized rate control for HWSNs. The proposed protocol consists of congestion avoidance and control. We distinguish between high and low priority traffics which are serviced upon their priority and QoS requirements. For high priority traffic, we control end to end delay constrains with node output scheduling weights. For low priority traffics firstly we avoid congestion using a new Active Queue Management (AQM) algorithm that uses distinct virtual queue's situation for a single physical queue in order to decide accepting or dropping the received packets from child nodes. If acceptance occurs for the incoming packet, congestion is detected by the proposed protocol using a three-state machine as well as a virtual queue status. Afterward, child’s sending rate is adjusted using an optimization function. Results of simulation indicate goal achievement for the protocol proposed.

Review of Queue Management in Packet Switched Networks

Queue management and congestion control are very important to the robustness and fairness of the Internet. In this paper, a new queue management algorithm, termed self adjustable choke (SAC) is proposed to achieve both fairness and congestion control in an Internet router. It is based on the well-known RED algorithm and a recently proposed CHOKe algorithm. The SAC scheme has kept the advantages of simplicity and lower processing cost of RED and CHOKe, while solving their unfair bandwidth allocation properties. In particular, the SAC scheme treats TCP and UDP flows differently, and can adaptively adjust its parameters according to the current traffic status. As a result, using the SAC, bandwidth is distributed evenly among different flows, no matter how different these flows are. This will be demonstrated and compared to CHOKe using experimental results under various traffic scenarios.

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.

Most of the existing congestion control works are suitable for the best effort flows, but not for the flows with quality of service (QoS) requirements. In this paper, by adopting equation-based congestion control (EBRC) for differentiated services instead of traditional TCP congestion control for best-effort service, a new congestion control mechanism supporting QoS requirements can be achieved. Three parts which the mechanism consists of are sender functionality, packet loss control mechanism in routers and receiver functionality. The details of design and implementation within this mechanism are investigated.

In order to mitigate the network congestion,a new active queue management algorithm named RQQM(Rate and Queue-based Queue Management) was proposed by Particle Swarm Optimization(PSO).In this algorithm,the actual queue length was deducted with PSO and variation factor,and the dropping strategy and dropping rate were given based on arrival rate and actual queue length.Then,a simulation with actual data was conducted to compare the algorithm performance between RQQM algorithm and RFED(Rate-based Fair Early Detection) algorithm,as well as ABLUE(Adaptive BLUE) algorithm.The results show that the dropping rate is greatly influenced by the utilization rate and buffer size,and the fairness of RQQM is much better than that of the other two algorithms,its average packet loss rate is decreased to 12.21%.