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

Congestion Control for Streaming Media

The Internet has assumed the role of the underlying communication network for applications such as file transfer, electronic mail, Web browsing and multimedia streaming. Multimedia streaming, in particular, is growing with the growth in power and connectivity of today\u27s computers. These Internet applications have a variety of network service requirements and traffic characteristics, which presents new challenges to the single best-effort service of today\u27s Internet. TCP, the de facto Internet transport protocol, has been successful in satisfying the needs of traditional Internet applications, but fails to satisfy the increasingly popular delay sensitive multimedia applications. Streaming applications often use UDP without a proper congestion avoidance mechanisms, threatening the well-being of the Internet. This dissertation presents an IP router traffic management mechanism, referred to as Crimson, that can be seamlessly deployed in the current Internet to protect well-behaving traffic from misbehaving traffic and support Quality of Service (QoS) requirements of delay sensitive multimedia applications as well as traditional Internet applications. In addition, as a means to enhance Internet support for multimedia streaming, this dissertation report presents design and evaluation of a TCP-Friendly and streaming-friendly transport protocol called the Multimedia Transport Protocol (MTP). Through a simulation study this report shows the Crimson network efficiently handles network congestion and minimizes queuing delay while providing affordable fairness protection from misbehaving flows over a wide range of traffic conditions. In addition, our results show that MTP offers streaming performance comparable to that provided by UDP, while doing so under a TCP-Friendly rate

A UNIQUE MATHEMATICAL QUEUING MODEL FOR WIRED AND WIRELESS NETWORKS

The de-facto protocol for transmitting data in wired and wireless networks is the Transmission Control Protocol/Internet Protocol (TCP/IP). While a lot of modifications have been done to adapt the TCP/IP protocol for wireless networks, a lot remains to be done about the bandwidth underutilization caused by network traffic control actions taken by active queue management controllers currently being implemented on modern routers. The main cause of bandwidth underutilization is uncertainties in network parameters. This is especially true for wireless networks. In this study, two unique mathematical models for queue management in wired and wireless networks are proposed. The models were derived using a recursive, thirdorder, discrete-time structure. The models are; the Model Predictive Controller (MPC) and the Self-Tuning Regulator (STR). The MPC was modeled to bear uncertainties in gain, poles and delay time. The STR, with an assigned closed-loop pole, was modeled to be very robust to varying network parameters. Theoretically, the proposed models deliver a performance in network traffic control that optimizes the use of available bandwidth and minimizes queue length and packet loss in wired and wireless networks

Improved congestion control for packet switched data networks and the Internet

Congestion control is one of the fundamental issues in computer networks. Without proper congestion control mechanisms there is the possibility of inefficient utilization of resources, ultimately leading to network collapse. Hence congestion control is an effort to adapt the performance of a network to changes in the traffic load without adversely affecting users perceived utilities. This thesis is a step in the direction of improved network congestion control. Traditionally the Internet has adopted a best effort policy while relying on an end-to-end mechanism. Complex functions are implemented by end users, keeping the core routers of network simple and scalable. This policy also helps in updating the software at the users' end. Thus, currently most of the functionality of the current Internet lie within the end users' protocols, particularly within Transmission Control Protocol (TCP). This strategy has worked fine to date, but networks have evolved and the traffic volume has increased many fold; hence routers need to be involved in controlling traffic, particularly during periods of congestion. Other benefits of using routers to control the flow of traffic would be facilitating the introduction of differentiated services or offering different qualities of service to different users. Any real congestion episode due to demand of greater than available bandwidth, or congestion created on a particular target host by computer viruses, will hamper the smooth execution of the offered network services. Thus, the role of congestion control mechanisms in modern computer networks is very crucial. In order to find effective solutions to congestion control, in this thesis we use feedback control system models of computer networks. The closed loop formed by TCPIIP between the end hosts, through intermediate routers, relies on implicit feedback of congestion information through returning acknowledgements. This feedback information about the congestion state of the network can be in the form of lost packets, changes in round trip time and rate of arrival of acknowledgements. Thus, end hosts can either execute reactive or proactive congestion control mechanisms. The former approach uses duplicate acknowledgements and timeouts as congestion signals, as done in TCP Reno, whereas the latter approach depends on changes in the round trip time, as in TCP Vegas. The protocols employing the second approach are still in their infancy as they cannot co-exist safely with protocols employing the first approach. Whereas TCP Reno and its mutations, such as TCP Sack, are presently widely used in computer networks, including the current Internet. These protocols require packet losses to happen before they can detect congestion, thus inherently leading to wastage of time and network bandwidth. Active Queue Management (AQM) is an alternative approach which provides congestion feedback from routers to end users. It makes a network to behave as a sensitive closed loop feedback control system, with a response time of one round trip time, congestion information being delivered to the end host to reduce data sending rates before actual packets losses happen. From this congestion information, end hosts can reduce their congestion window size, thus pumping fewer packets into a congested network until the congestion period is over and routers stop sending congestion signals. Keeping both approaches in view, we have adopted a two-pronged strategy to address the problem of congestion control. They are to adapt the network at its edges as well as its core routers. We begin by introducing TCPIIP based computer networks and defining the congestion control problem. Next we look at different proactive end-to-end protocols, including TCP Vegas due to its better fairness properties. We address the incompatibility problem between TCP Vegas and TCP Reno by using ECN based on Random Early Detection (RED) algorithm to adjust parameters of TCP Vegas. Further, we develop two alternative algorithms, namely optimal minimum variance and generalized optimal minimum variance, for fair end-to-end protocols. The relationship between (p, 1) proportionally fair algorithm and the generalized algorithm is investigated along with conditions for its stable operation. Noteworthy is a novel treatment of the issue of transient fairness. This represents the work done on congestion control at the edges of network. Next, we focus on router-based congestion control algorithms and start with a survey of previous work done in that direction. We select the RED algorithm for further work due to it being recommended for the implementation of AQM. First we devise a new Hybrid RED algorithm which employs instantaneous queue size along with an exponential weighted moving average queue size for making decisions about packet marking/dropping, and adjusts the average value during periods of low traffic. This algorithm improves the link utilization and packet loss rate as compared to basic RED. We further propose a control theory based Auto-tuning RED algorithm that adapts to changing traffic load. This algorithm can clamp the average queue size to a desired reference value which can be used to estimate queuing delays for Quality of Service purposes. As an alternative approach to router-based congestion control, we investigate Proportional, Proportional-Integral (PI) and Proportional-Integral-Derivative (PID) principles based control algorithms for AQM. New control-theoretic RED and frequency response based PI and PID control algorithms are developed and their performance is compared with that of existing algorithms. Later we transform the RED and PI principle based algorithms into their adaptive versions using the well known square root of p formula. The performance of these load adaptive algorithms is compared with that of the previously developed fixed parameter algorithms. Apart from some recent research, most of the previous efforts on the design of congestion control algorithms have been heuristic. This thesis provides an effective use of control theory principles in the design of congestion control algorithms. We develop fixed-parameter-type feedback congestion control algorithms as well as their adaptive versions. All of the newly proposed algorithms are evaluated by using ns-based simulations. The thesis concludes with a number of research proposals emanating from the work reported

A UNIQUE MATHEMATICAL QUEUING MODEL FOR WIRED AND WIRELESS NETWORKS

The de-facto protocol for transmitting data in wired and wireless networks is the Transmission Control Protocol/Internet Protocol (TCP/IP). While a lot of modifications have been done to adapt the TCP/IP protocol for wireless networks, a lot remains to be done about the bandwidth underutilization caused by network traffic control actions taken by active queue management controllers currently being implemented on modern routers. The main cause of bandwidth underutilization is uncertainties in network parameters. This is especially true for wireless networks. In this study, two unique mathematical models for queue management in wired and wireless networks are proposed. The models were derived using a recursive, thirdorder, discrete-time structure. The models are; the Model Predictive Controller (MPC) and the Self-Tuning Regulator (STR). The MPC was modeled to bear uncertainties in gain, poles and delay time. The STR, with an assigned closed-loop pole, was modeled to be very robust to varying network parameters. Theoretically, the proposed models deliver a performance in network traffic control that optimizes the use of available bandwidth and minimizes queue length and packet loss in wired and wireless networks

Parameter self-tuning in internet congestion control

Active Queue Management (AQM) aims to achieve high link utilization, low queuing delay and low loss rate in routers. However, it is difficult to adapt AQM parameters to constantly provide desirable transient and steady-state performance under highly dynamic network scenarios. They need to be a trade-off made between queuing delay and utilization. The queue size would become unstable when round-trip time or link capacity increases, or would be unnecessarily large when round-trip time or link capacity decreases. Effective ways of adapting AQM parameters to obtain good performance have remained a critical unsolved problem during the last fifteen years. This thesis firstly investigates existing AQM algorithms and their performance. Based on a previously developed dynamic model of TCP behaviour and a linear feedback model of TCP/RED, Auto-Parameterization RED (AP-RED) is proposed which unveils the mechanism of adapting RED parameters according to measurable network conditions. Another algorithm of Statistical Tuning RED (ST-RED) is developed for systematically tuning four key RED parameters to control the local stability in response to the detected change in the variance of the queue size. Under variable network scenarios like round-trip time, link capacity and traffic load, no manual parameter configuration is needed. The proposed ST-RED can adjust corresponding parameters rapidly to maintain stable performance and keep queuing delay as low as possible. Thus the sensitivity of RED's performance to different network scenarios is removed. This Statistical Tuning algorithm can be applied to a PI controller for AQM and a Statistical Tuning PI (ST-PI) controller is also developed. The implementation of ST-RED and ST-PI is relatively straightforward. Simulation results demonstrate the feasibility of ST-RED and ST-PI and their capabilities to provide desirable transient and steady-state performance under extensively varying network conditions.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

A multi-objective particle swarm optimized fuzzy logic congestion detection and dual explicit notification mechanism for IP networks.

Thesis (M.Sc.Eng.)-University of KwaZulu-Natal, 2006.The Internet has experienced a tremendous growth over the past two decades and with that growth have come severe congestion problems. Research efforts to alleviate the congestion problem can broadly be classified into three groups: Cl) Router based congestion detection; (2) Generation and transmission of congestion notification signal to the traffic sources; (3) End-to-end algorithms which control the flow of traffic between the end hosts. This dissertation has largely addressed the first two groups which are basically router initiated. Router based congestion detection mechanisms, commonly known as Active Queue Management (AQM), can be classified into two groups: conventional mathematical analytical techniques and fuzzy logic based techniques. Research has shown that fuzzy logic techniques are more effective and robust compared to the conventional techniques because they do not rely on the availability of a precise mathematical model of Internet. They use linguistic knowledge and are, therefore, better placed to handle the complexities associated with the non-linearity and dynamics of the Internet. In spite of all these developments, there still exists ample room for improvement because, practically, there has been a slow deployment of AQM mechanisms. In the first part of this dissertation, we study the major AQM schemes in both the conventional and the fuzzy logic domain in order to uncover the problems that have hampered their deployment in practical implementations. Based on the findings from this study, we model the Internet congestion problem as a multi-objective problem. We propose a Fuzzy Logic Congestion Detection (FLCD) which synergistically combines the good characteristics of the fuzzy approaches with those of the conventional approaches. We design the membership functions (MFs) of the FLCD algorithm automatically by using Multi-objective Particle Swarm Optimization (MOPSO), a population based stochastic optimization algorithm. This enables the FLCD algorithm to achieve optimal performance on all the major objectives of Internet congestion control. The FLCD algorithm is compared with the basic Fuzzy Logic AQM and the Random Explicit Marking (REM) algorithms on a best effort network. Simulation results show that the FLCD algorithm provides high link utilization whilst maintaining lower jitter and packet loss. It also exhibits higher fairness and stability compared to its basic variant and REM. We extend this concept to Proportional Differentiated Services network environment where the FLCD algorithm outperforms the traditional Weighted RED algorithm. We also propose self learning and organization structures which enable the FLCD algorithm to achieve a more stable queue, lower packet losses and UDP traffic delay in dynamic traffic environments on both wired and wireless networks. In the second part of this dissertation, we present the congestion notification mechanisms which have been proposed for wired and satellite networks. We propose an FLCD based dual explicit congestion notification algorithm which combines the merits of the Explicit Congestion Notification (ECN) and the Backward Explicit Congestion Notification (BECN) mechanisms. In this proposal, the ECN mechanism is invoked based on the packet marking probability while the BECN mechanism is invoked based on the BECN parameter which helps to ensure that BECN is invoked only when congestion is severe. Motivated by the fact that TCP reacts to tbe congestion notification signal only once during a round trip time (RTT), we propose an RTT based BECN decay function. This reduces the invocation of the BECN mechanism and resultantly the generation of reverse traffic during an RTT. Compared to the traditional explicit notification mechanisms, simulation results show that the new approach exhibits lower packet loss rates and higher queue stability on wired networks. It also exhibits lower packet loss rates, higher good-put and link utilization on satellite networks. We also observe that the BECN decay function reduces reverse traffic significantly on both wired and satellite networks while ensuring that performance remains virtually the same as in the algorithm without BECN traffic reduction.Print copy complete; page numbering of 105-108 incorrect

Congestion Control Based on Multiple Model Adaptive Control

The congestion controller based on the multiple model adaptive control is designed for the network congestion in TCP/AQM network. As the conventional congestion control is sensitive to the variable network condition, the adaptive control method is adopted in our congestion control. The multiple model adaptive control is introduced in this paper based on the weight calculation instead of the parameter estimation in past adaptive control. The model set is composed by the dynamic model based on the fluid flow. And three “local” congestion controllers are nonlinear output feedback controller based on variable RTT, H2 output feedback controller, and proportional-integral controller, respectively. Ns-2 simulation results in section 4 indicate that the proposed algorithm restrains the congestion in variable network condition and maintains a high throughput together with a low packet drop ratio