Combined Model for Congestion Control

The growth of multimedia applications on the Internet made at least one fifth of the total network traffic to run over UDP. Unlike TCP, UDP is unresponsive to network congestion. This may cause, inter alia, bandwidth starvation of responsive flows, severe and prolonged congestions or, in the worst-case scenario, a congestion collapse. Hence, the coexistence of both protocols on fair-share premises converges towards impossibility. The paper deals with a new approach to solving the problem of taming down the unresponsive flows. By using some of the desirable properties of mobile agents, the system is able to control the influx of non-TCP or unresponsive flows into the network. Various functions performed by mobile agents monitor non-TCP flows, calculate sending rates and modify their intensity according to the needs of the network to attain as good performance as it is possible

Optimization and Performance Analysis of High Speed Mobile Access Networks

The end-to-end performance evaluation of high speed broadband mobile access networks is the main focus of this work. Novel transport network adaptive flow control and enhanced congestion control algorithms are proposed, implemented, tested and validated using a comprehensive High speed packet Access (HSPA) system simulator. The simulation analysis confirms that the aforementioned algorithms are able to provide reliable and guaranteed services for both network operators and end users cost-effectively. Further, two novel analytical models one for congestion control and the other for the combined flow control and congestion control which are based on Markov chains are designed and developed to perform the aforementioned analysis efficiently compared to time consuming detailed system simulations. In addition, the effects of the Long Term Evolution (LTE) transport network (S1and X2 interfaces) on the end user performance are investigated and analysed by introducing a novel comprehensive MAC scheduling scheme and a novel transport service differentiation model

An analysis of factors related to areawide highway traffic congestion

Roadway expansion is a traditional strategy used to accommodate travel demand and reduce traffic congestion in urban areas. The potential negative effects of roadway expansion and mounting concerns over urban area congestion, however, have spurred research into the factors that control congestion. The aim of this study is to investigate the relationship between traffic congestion, travel demand, and supply of roadways. To accomplish this goal, data for the top 138 urbanized areas (by population) were assembled for developing a least squares regression model. Travel Rate Index,a congestion measure developed by researchers at the Texas Transportation Institute, was selected as the response (dependent) vanable. A variety of explanatory variables were used to address highway and transit supply and travel demand related factors. The partial regression coefficients measured the effect of each explanatory (independent) variable on congestion (as measured by Travel Rate Index), holding all other independent variables constant. The results of the multiple regression analysis indicated a negative correlation between freeway lane miles and Combined Travel Rate Index. Additionally, a strong positive correlation was observed between Combined Travel Rate Index and population density and net land area, respectively. A positive correlation was observed between Combined Travel Rate Index and bus transit service revenue miles Principal arterial lane miles and rail transit revenue miles variables were not observed to be significant for explaining traffic congestion and were removed entirely during the stepwise regression The results indicated that the best predictors among the tested variables were freeway lane miles, population density, net land area, and bus revenue miles. When used together, these predictors accounted for approximately 61% of the total variation in the dependent variable, Combined Travel Rate Index. Overall, population and net land area accounted for the bulk of the variation in congestion level (Travel Rate Index)

Doctor of Philosophy

dissertationAs the nation's traffic system becomes more congested for various periods of the day, more research in the area of intelligent transportation systems is needed. Traditional solutions of adding more highways and widening the existing system are not feasible anymore due to rapidly increasing demand and lack of room for expansion. The national interest is therefore focused on congestion mitigation methods that promote efficient use of existing infrastructure. Some of the key aspects of congestion management techniques include Intelligent Transportation Systems (ITS) elements. These ITS elements can play a role in drivers' interaction, route choice, and traffic controls. Combined Traffic Assignment and Control (CTAC) framework-based models can capture the ITS elements- based control-driver interaction in traffic systems. The CTAC method has been the topic of scientific research for the last three decades. Several solution algorithms, model formulations, and implementation efforts have been well documented. Although proven in research, the use of the combined traffic assignment and control modeling framework is rare in practice. Typically, the engineering practice tends to keep Traffic Assignment and Control Optimization processes separate. By doing so, the control-driver interaction in the traffic system is ignored. Previous research found that CTAC models could capture the control-driver interaction and the combined modeling framework should be used in practice

Asynchronous networked MPC with ISM for uncertain nonlinear systems

A model-based event-triggered control scheme for nonlinear constrained continuous-time uncertain systems in networked configuration is presented in this paper. It is based on the combined use of Model Predictive Control (MPC) and Integral Sliding Mode (ISM) control, and it is oriented to reduce the packets transmission over the network both in the direct path and in the feedback path, in order to avoid network congestion. The key elements of the proposed control scheme are the ISM local control law, the MPC remote controller, a smart sensor and a smart actuator, both containing a copy of the nominal model of the plant. The role of the ISM control law is to compensate matched uncertainties, without amplifying the unmatched ones. The MPC controller with tightened constraints generates the control component oriented to comply with state and control requirements, and is asynchronous since the underlying constrained optimization problem is solved only when a triggering event occurs. In the paper, the robustness properties of the controlled system are theoretically analyzed, proving the regional input-tostate practical stability of the overall control scheme

A Taxonomy for Congestion Control Algorithms in Vehicular Ad Hoc Networks

One of the main criteria in Vehicular Ad hoc Networks (VANETs) that has attracted the researchers' consideration is congestion control. Accordingly, many algorithms have been proposed to alleviate the congestion problem, although it is hard to find an appropriate algorithm for applications and safety messages among them. Safety messages encompass beacons and event-driven messages. Delay and reliability are essential requirements for event-driven messages. In crowded networks where beacon messages are broadcasted at a high number of frequencies by many vehicles, the Control Channel (CCH), which used for beacons sending, will be easily congested. On the other hand, to guarantee the reliability and timely delivery of event-driven messages, having a congestion free control channel is a necessity. Thus, consideration of this study is given to find a solution for the congestion problem in VANETs by taking a comprehensive look at the existent congestion control algorithms. In addition, the taxonomy for congestion control algorithms in VANETs is presented based on three classes, namely, proactive, reactive and hybrid. Finally, we have found the criteria in which fulfill prerequisite of a good congestion control algorithm

A Differential Game Modeling Approach to Dynamic Traffic Assignment and Signal Control

This paper addresses a theoretical issue related to combined dynamic traffic assignment and signal control under conditions of congestion through a brief review of previous research and the discussion on interaction between dynamic traffic assignment and signal control. The dynamic characteristics of the interaction are approached using a differential game modeling approach here to formulate the decision-making process for solving the problem inherent in this combination. Specifically, the combined dynamic traffic assignment and signal control problem is formulated as a leader−follower differential game, where a leader and multiple followers engage interactively to finding optimal strategies under the assumption of an openloop information structure. Discretization in time is used to find a numerical solution for the proposed game model, and a simulated annealing algorithm is applied to obtain optimal strategies. Finally, a simulation study is conducted on a simple traffic network in which numerical results demonstrate the effectiveness of the proposed approach

Controlling Network Latency in Mixed Hadoop Clusters: Do We Need Active Queue Management?

With the advent of big data, data center applications are processing vast amounts of unstructured and semi-structured data, in parallel on large clusters, across hundreds to thousands of nodes. The highest performance for these batch big data workloads is achieved using expensive network equipment with large buffers, which accommodate bursts in network traffic and allocate bandwidth fairly even when the network is congested. Throughput-sensitive big data applications are, however, often executed in the same data center as latency-sensitive workloads. For both workloads to be supported well, the network must provide both maximum throughput and low latency. Progress has been made in this direction, as modern network switches support Active Queue Management (AQM) and Explicit Congestion Notifications (ECN), both mechanisms to control the level of queue occupancy, reducing the total network latency. This paper is the first study of the effect of Active Queue Management on both throughput and latency, in the context of Hadoop and the MapReduce programming model. We give a quantitative comparison of four different approaches for controlling buffer occupancy and latency: RED and CoDel, both standalone and also combined with ECN and DCTCP network protocol, and identify the AQM configurations that maintain Hadoop execution time gains from larger buffers within 5%, while reducing network packet latency caused by bufferbloat by up to 85%. Finally, we provide recommendations to administrators of Hadoop clusters as to how to improve latency without degrading the throughput of batch big data workloads.The research leading to these results has received funding from the European Unions Seventh Framework Programme (FP7/2007–2013) under grant agreement number 610456 (Euroserver). The research was also supported by the Ministry of Economy and Competitiveness of Spain under the contracts TIN2012-34557 and TIN2015-65316-P, Generalitat de Catalunya (contracts 2014-SGR-1051 and 2014-SGR-1272), HiPEAC-3 Network of Excellence (ICT- 287759), and the Severo Ochoa Program (SEV-2011-00067) of the Spanish Government.Peer ReviewedPostprint (author's final draft