Analysis of TCP-AQM Interaction Via Periodic Optimization and Linear Programming: The Case of Sigmoidal Utility Function
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Sigmoid function
Random early detection
Since random early detection (RED) was proposed in 1993, many active queue management (AQM) algorithms have been proposed to support better end-to-end Transmission Control Protocol (TCP) congestion control. In this article, the authors introduce and analyze a feedback control model of the TCP/AQM dynamics. Then they suggest the concept of an AQM algorithm that can detect and avoid congestion proactively. Finally, they propose the proportional-integral (PI) proportional-derivative (PD) controller using proportional-integral-derivative (PID) feedback control to overcome the reactive control behavior of existing AQM proposals. The PI-PD controller is able to provide proactive congestion avoidance and control using an adaptive congestion indicator and a control function. A comparative simulation study under a variety of network environments shows that the PI-PD controller outperforms RED and the PI controller in terms of the queue length dynamics, the packet loss rates, and the link utilization.
Random early detection
Explicit Congestion Notification
Packet loss
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Most of the traffic on the Internet is depend upon the Transmission Control Protocol (TCP), so the performance of TCP is directly related to Internet. Many TCP variants are developed and modified according to the environment and communication needs. Most of current TCP variants have set of algorithms which control the congestion in critical situations and maintain the throughput and efficiency of network. Now a day’s TCP is facing fast growth of Internet with the demands of faster data communication techniques on high speed links. In last 15 years many computer systems and cellular networks become linked together with protocol stack used in TCP. TCP variants with different congestion control techniques are working in different operating systems but a very small number of techniques are able to minimize the congestion in the network. This paper presents a survey on end-to-end congestion control techniques used in different TCP versions. The main purpose of this study is to review the characteristics and behavior of TCP variants with different techniques to control the congestion in the different network scenarios.
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HSTCP
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Active queue management (AQM) has been widely used for congestion avoidance in transmission control protocol (TCP) networks. Although numerous AQM schemes have been proposed to regulate a queue size close to a reference level, most of them are incapable of adequately adapting to TCP network dynamics due to TCP's non-linearity and time-varying stochastic properties. To alleviate these problems, we introduce an AQM technique based on a dynamic neural network using the back-propagation (BP) algorithm. The dynamic neural network is designed to perform as a robust adaptive feedback controller for TCP dynamics after an adequate training period. We evaluate the performances of the proposed neural network AQM approach using simulation experiments. The proposed approach yields superior performance with faster transient time, larger throughput, and higher link utilization compared to two existing schemes: random early detection (RED) and proportional-integral (PI)-based AQM. The neural AQM outperformed PI control and RED, especially in transient state and TCP dynamics variation.
Random early detection
Network Dynamics
CUBIC TCP
Transient (computer programming)
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Current Internet traffic is carried mainly by the Transmission Control Protocol (TCP), and the performance of the Internet depends strongly on how well TCP works. Detecting and mitigating the congestion is one of the main tasks of this protocol, in fact, different TCP versions are defined by their congestion control algorithm. Also, Active Queue Management (AQM) algorithms share the same goal of congestion mitigation with TCP, in particular, the most effective congestion control occurs when AQM and TCP work together. This paper presents a brief survey and a cross-comparison of the latest and most important TCP and AQM variants, then provides an evaluation of a different kind of performance (multiple Round-Trip Time, long delay, different congestion level, etc.) on the ns-3 network simulator over various types of environments. In a shared bottleneck, the choice of the TCP-AQM couple to adopt is crucial; we show that the results are not univocal, the "golden couple" depends on the performance that we want to boost and on the environment that we have to deal with.
CUBIC TCP
H-TCP
TCP Westwood plus
Explicit Congestion Notification
HSTCP
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In this survey, we first review the concept of congestion control with a focus on the transmission control protocol/Internet protocol (TCP/IP). We describe many recently proposed algorithms to combat congestion and improve performance, particularly active queue management (AQM) algorithms such as random early detection (RED) and its variants. We then survey control-theoretic analysis and design of TCP congestion control with an AQM scheme. In addition, we discuss three problems associated with AQM proposals: parameter setting, the insensitivity to the input traffic load variation, and the mismatch between macroscopic and microscopic behavior of queue length dynamics. As alternatives to AQM algorithms, we also survey architectural approaches such as modification of source or network algorithms, and economic approaches including pricing or optimization of allocated resources. Finally, we list many open issues that persist in the design, operation, and control of the Internet.
Random early detection
Explicit Congestion Notification
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This paper is concerned with congestion control for dynamic TCP/AQM networks with random delays. A robust congestion control algorithm is presented based on the H∞ approach, and the system performance is analyzed via linear matrix inequalities (LMIs). Simulation results illustrate the effectiveness of the proposed algorithm.
Random early detection
H-TCP
CUBIC TCP
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Random early detection
Transient (computer programming)
Network Dynamics
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