Enhancing AQM to combat wireless losses

In order to maintain a small, stable backlog at the router buffer, active queue management (AQM) algorithms drop packets probabilistically at the onset of congestion, leading to backoffs by Transmission Control Protocol (TCP) flows. However, wireless losses may be misinterpreted as congestive losses and induce spurious backoffs. In this paper, we raise the basic question: Can AQM maintain a stable, small backlog under wireless losses? We find that the representative AQM, random early detection (RED), fails to maintain a stable backlog under time-varying wireless losses. We find that the key to resolving the problem is to robustly track the backlog to a preset reference level, and apply the control-theoretic vehicle, internal model principle, to realize such tracking. We further devise the integral controller (IC) as an embodiment of the principle. Our simulation results show that IC is robust against time-varying wireless losses under various network scenarios. © 2012 IEEE.published_or_final_versio

Congestion mitigation in LTE base stations using radio resource allocation techniques with TCP end to end transport

As of 2019, Long Term Evolution (LTE) is the chosen standard for most mobile and fixed wireless data communication. The next generation of standards known as 5G will encompass the Internet of Things (IoT) which will add more wireless devices to the network. Due to an exponential increase in the number of wireless subscriptions, in the next few years there is also an expected exponential increase in data traffic. Most of these devices will use Transmission Control Protocol (TCP) which is a type of network protocol for delivering internet data to users. Due to its reliability in delivering data payload to users and congestion management, TCP is the most common type of network protocol used. However, the ability for TCP to combat network congestion has certain limitations especially in a wireless network. This is due to wireless networks not being as reliable as fixed line networks for data delivery because of the use of last mile radio interface. LTE uses various error correction techniques for reliable data delivery over the air-interface. These cause other issues such as excessive latency and queuing in the base station leading to degradation in throughput for users and congestion in the network. Traditional methods of dealing with congestion such as tail-drop can be inefficient and cumbersome. Therefore, adequate congestion mitigation mechanisms are required. The LTE standard uses a technique to pre-empt network congestion by a mechanism known as Discard Timer. Additionally, there are other algorithms such as Random Early Detection (RED) that also are used for network congestion mitigation. However, these mechanisms rely on configured parameters and only work well within certain regions of operation. If the parameters are not set correctly then the TCP links can experience congestion collapse. In this thesis, the limitations of using existing LTE congestion mitigation mechanisms such as Discard Timer and RED have been explored. A different mechanism to analyse the effects of using control theory for congestion mitigation has been developed. Finally, congestion mitigation in LTE networks has been addresses using radio resource allocation techniques with non-cooperative game theory being an underlying mathematical framework. In doing so, two key end-to-end performance measurements considered for measuring congestion for the game theoretic models were identified which were the total end-to-end delay and the overall throughput of each individual TCP link. An end to end wireless simulator model with the radio access network using LTE and a TCP based backbone to the end server was developed using MATLAB. This simulator was used as a baseline for testing each of the congestion mitigation mechanisms. This thesis also provides a comparison and performance evaluation between the congestion mitigation models developed using existing techniques (such as Discard Timer and RED), control theory and game theory. As of 2019, Long Term Evolution (LTE) is the chosen standard for most mobile and fixed wireless data communication. The next generation of standards known as 5G will encompass the Internet of Things (IoT) which will add more wireless devices to the network. Due to an exponential increase in the number of wireless subscriptions, in the next few years there is also an expected exponential increase in data traffic. Most of these devices will use Transmission Control Protocol (TCP) which is a type of network protocol for delivering internet data to users. Due to its reliability in delivering data payload to users and congestion management, TCP is the most common type of network protocol used. However, the ability for TCP to combat network congestion has certain limitations especially in a wireless network. This is due to wireless networks not being as reliable as fixed line networks for data delivery because of the use of last mile radio interface. LTE uses various error correction techniques for reliable data delivery over the air-interface. These cause other issues such as excessive latency and queuing in the base station leading to degradation in throughput for users and congestion in the network. Traditional methods of dealing with congestion such as tail-drop can be inefficient and cumbersome. Therefore, adequate congestion mitigation mechanisms are required. The LTE standard uses a technique to pre-empt network congestion by a mechanism known as Discard Timer. Additionally, there are other algorithms such as Random Early Detection (RED) that also are used for network congestion mitigation. However, these mechanisms rely on configured parameters and only work well within certain regions of operation. If the parameters are not set correctly then the TCP links can experience congestion collapse. In this thesis, the limitations of using existing LTE congestion mitigation mechanisms such as Discard Timer and RED have been explored. A different mechanism to analyse the effects of using control theory for congestion mitigation has been developed. Finally, congestion mitigation in LTE networks has been addresses using radio resource allocation techniques with non-cooperative game theory being an underlying mathematical framework. In doing so, two key end-to-end performance measurements considered for measuring congestion for the game theoretic models were identified which were the total end-to-end delay and the overall throughput of each individual TCP link. An end to end wireless simulator model with the radio access network using LTE and a TCP based backbone to the end server was developed using MATLAB. This simulator was used as a baseline for testing each of the congestion mitigation mechanisms. This thesis also provides a comparison and performance evaluation between the congestion mitigation models developed using existing techniques (such as Discard Timer and RED), control theory and game theory

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