GA-PSO-Optimized Neural-Based Control Scheme for Adaptive Congestion Control to Improve Performance in Multimedia Applications

Active queue control aims to improve the overall communication network throughput while providing lower delay and small packet loss rate. The basic idea is to actively trigger packet dropping (or marking provided by explicit congestion notification (ECN)) before buffer overflow. In this paper, two artificial neural networks (ANN)-based control schemes are proposed for adaptive queue control in TCP communication networks. The structure of these controllers is optimized using genetic algorithm (GA) and the output weights of ANNs are optimized using particle swarm optimization (PSO) algorithm. The controllers are radial bias function (RBF)-based, but to improve the robustness of RBF controller, an error-integral term is added to RBF equation in the second scheme. Experimental results show that GA- PSO-optimized improved RBF (I-RBF) model controls network congestion effectively in terms of link utilization with a low packet loss rate and outperform Drop Tail, proportional-integral (PI), random exponential marking (REM), and adaptive random early detection (ARED) controllers.Comment: arXiv admin note: text overlap with arXiv:1711.0635

On Designing Lyapunov-Krasovskii Based AQM for Routers Supporting TCP Flows

For the last few years, we assist to a growing interest of designing AQM (Active Queue Management) using control theory. In this paper, we focus on the synthesis of an AQM based on the Lyapunov theory for time delay systems. With the help of a recently developed Lyapunov-Krasovskii functional and using a state space representation of a linearized fluid model of TCP, two robust AQMs stabilizing the TCP model are constructed. Notice that our results are constructive and the synthesis problem is reduced to a convex optimization scheme expressed in terms of linear matrix inequalities (LMIs). Finally, an example extracted from the literature and simulations via {\it NS simulator} support our study

Design of Feedback Controls Supporting TCP Based on the State–Space Approach

This paper investigates how to design feedback controls supporting transmission control protocol (TCP) based on the state-space approach for the linearized system of the well-known additive increase multiplicative decrease (AIMD) dynamic model. We formulate the feedback control design problem as state-space models without assuming its structure in advance. Thereby, we get three results that have not been observed by previous studies on the congestion control problem. 1) In order to fully support TCP, we need a proportional-derivative (PD)-type state-feedback control structure in terms of queue length (or RTT: round trip time). This backs up the conjecture in the networking literature that the AQM RED is not enough to control TCP dynamic behavior, where RED can be classified as a P-type AQM (or as an output feedback control for the linearized AIMD model). 2) In order to fully support TCP in the presence of delays, we derive delay-dependent feedback control structures to compensate for delays explicitly under the assumption that RTT, capacity and number of sources are known, where all existing AQMs including RED, REM/PI and AVQ are delay-independent controls. 3) In an attempt to interpret different AQM structures in a unified manner rather than to compare them via simulations, we propose a PID-type mathematical framework using integral control action. As a performance index to measure the deviation of the closed-loop system from an equilibrium point, we use a linear quadratic (LQ) cost of the transients of state and control variables such as queue length, aggregate rate, jitter in the aggregate rate, and congestion measure. Stabilizing gains of the feedback control structures are obtained minimizing the LQ cost. Then, we discuss the impact of the control structure on performance using the PID-type mathematical framework. All results are extended to the case of multiple links and heterogeneous delays