Power quality has a great effect on the operation of system loads. To analyze its effects and the possible economic losses due to system disturbances, there is an immediate need of a power quality monitoring system. With an effective communication system, network disturbance data can be gathered and analyzed efficiently such that outage duration and its consequent losses can be reduced. This thesis presents communication network performance simulation results of different types of communication schemes used in a power quality monitoring system. Discrete event simulation method is used to study the end-to-end delay times of different communication architectures. Based on these simulation results, system designers can choose the best option to meet their data communication requirements in power quality monitoring.

The objective of this research is to develop innovative techniques for modeling and control of network congestion. Most existing network controls have discontinuous actions, but such discontinuity in control actions is commonly omitted in analytical models, and instead continuous models were widely adopted in the literature. This approximation works well under certain conditions, but it does cause significant discrepancy in creating robust, responsive control solutions for congestion management. In this dissertation, I investigated three major topics. I proposed a generic discontinuous congestion control model and its design methodology to guarantee asymptotic stability and eliminate traffic oscillation, based on the sliding mode control (SMC) theory. My scheme shows that discontinuity plays a crucial role in optimization of the I-D based congestion control algorithms. When properly modeled, the simple I-D control laws can be made highly robust to parameter and model uncertainties. I discussed applicability of this model to some existing flow or congestion control schemes, e.g. XON/XOFF, rate and window based AIMD, RED, etc. It can also be effectively applied to design of detection and defense of distributed denial of service (DDoS) attacks. DDoS management can be considered a special case of the flow control problem. Based on my generic discontinuous congestion control model, I developed a backward-propagation feedback control strategy for DDoS detection and defense. It not only prevents DDoS attacks but also provides smooth traffic and bounded queue size. Another application of the congestion control algorithms is design of private group communication networks. I proposed a new technique for protection of group communications by concealment of sender-recipient pairs. The basic approach is to fragment and disperse encrypted messages into packets to be transported along different paths, so that the adversary cannot efficiently determine the source/recipient of a message without correct ordering of all packets. Packet flows among nodes are made balanced, to eliminate traffic patterns related to group activities. I proposed a sliding window-based flow control scheme to control transmission of payload and dummy packets. My algorithms allow flexible tradeoff between traffic concealment and performance requirement.

Virtualization environments have become basic building blocks in consolidated data centers and cloud computing infrastructures. By running multiple virtual machines (VMs) in a shared physical machine, virtualization achieves high utilization of hardware resources and provides strong isolation between virtual machines. This dissertation discusses the implementation and the evaluation of an approach, called kernel service outsourcing, which improves the performance and the reliability of guest systems in the virtualized, multi-kernel environments. Kernel service outsourcing allows applications to exploit OS services from an external kernel existing in the shared system, not limiting application OS service requests to the local kernel. Because external kernels may provide more efficient services than the local kernel does, kernel service outsourcing provides new opportunities with applications in the guest OS for better performance. In addition, we apply the kernel service outsourcing technique to implement natural diversity, improving the reliability of the virtualized systems. We present two major benefits of kernel service outsourcing. First, we show that I/O service outsourcing can significantly improve the I/O performance of guest OSes by up to several times. In some important cases, the performance of network applications in the guest OS using network outsourcing was comparable to that of native OS (Linux). We also apply kernel service outsourcing between Windows and Linux, and show that kernel service outsourcing is viable even with two heterogeneous OS kernels. In addition, we study further performance optimization techniques that can be achieved in the external kernel when certain OS services are outsourced to the external kernel. The second benefit of kernel service outsourcing is to improve system reliability through natural diversity created by the combination of different kinds of the OS kernel implementations. Because OS services can be outsourced to different versions or even heterogeneous types of OS kernel for equivalent functions, malicious attacks that aim to exploit certain vulnerabilities in specific versions of OS kernels would not succeed in the outsourced kernels. Our case studies with Windows and Linux show that kernel service outsourcing was able to prevent the malicious attacks designed to exploit implementation-dependent vulnerabilities in the OSes from becoming successful in the outsourced systems.

Thesis (Ph. D.)--School of Electrical and Computer Engineering, Georgia Institute of Technology, 2004. Directed by Nikil Jayant. / Vita. Includes bibliographical references (leaves 150-160).

In wireless communication systems, receivers have generally been designed under the assumption that the additive noise in system is Gaussian. Wireless receivers, however, are affected by radio frequency interference (RFI) generated from various sources such as other wireless users, switching electronics, and computational platforms. RFI is well modeled using non-Gaussian impulsive statistics and can severely degrade the communication performance of wireless receivers designed under the assumption of additive Gaussian noise. Methods to avoid, cancel, or reduce RFI have been an active area of research over the past three decades. In practice, RFI cannot be completely avoided or canceled at the receiver. This dissertation derives the statistics of the residual RFI and utilizes them to analyze and improve the communication performance of wireless receivers. The primary contributions of this dissertation are to (i) derive instantaneous statistics of co-channel interference in a field of Poisson and Poisson-Poisson clustered interferers, (ii) characterize throughput, delay, and reliability of decentralized wireless networks with temporal correlation, and (iii) design pre-filters to mitigate RFI in wireless receivers. / text

This thesis presents several decentralized, learning-based algorithms for on-line routing of bandwidth guaranteed paths. The presented routing algorithms do not need any a-priori knowledge of traffic demand; they use only their locally observed events and update their routing policy using learning schemes. The employed learning algorithms are either learning automata or the multi-armed bandit algorithms. We investigate the asymptotic behavior of the proposed routing algorithms and prove the convergence of one of them to the user equilibrium. Discrete event simulation results show the merit of these algorithms in terms of improving the resource utilization and increasing the network admissibility compared with shortest path routing. / We investigate the performance degradation due to decentralized routing as opposed to centralized optimal routing policies in practical scenarios. The system optimal and the Nash bargaining solutions are two centralized benchmarks used in this study. We provide nonlinear programming formulations of these problems along with a distributed recursive approach to compute the solutions. An on-line partially-decentralized control architecture is also proposed to achieve the system optimal and the Nash bargaining solution performances. Numerical results in some practical scenarios with well engineered networks, where the network resources and traffic demand are well matched, indicate that decentralized learning techniques provide efficient, stable and scalable approaches for routing the bandwidth guaranteed paths. / In the context of on-line learning, we propose a new algorithm to track the best action-selection policy when it abruptly changes over time. The proposed algorithm employs change detection mechanisms to detect the sudden changes and restarts the learning process on the detection of an abrupt change. The performance analysis of this study reveals that when all the changes are detectable by the change detection mechanism, the proposed tracking the best action-selection policy algorithm is rate optimal. On-line routing of bandwidth guaranteed paths with the potential occurrence of network shocks such as significant changes in the traffic demand is one of the applications of the devised algorithm. Simulation results show the merit of the proposed algorithm in tracking the optimal routing policy when it abruptly changes.

Despite the persistent change and growth that characterizes the Internet, the Transmission Control Protocol (TCP) still dominates at the transport layer, carrying more than 90\% of the global traffic. Despite its astonishing success, it has been observed that TCP can cause poor end-to-end performance, especially for large transfers and in network paths with high bandwidth-delay product. In this thesis, we focus on mechanisms that can address key problems in TCP performance, without any modification in the protocol itself. This evolutionary approach is important in practice, as the deployment of clean-slate transport protocols in the Internet has been proved to be extremely difficult. Specifically, we identify a number of TCP-related problems that can cause poor end-to-end performance. These problems include poorly dimensioned socket buffer sizes at the end-hosts, suboptimal buffer sizing at routers and switches, and congestion unresponsive TCP traffic aggregates. We propose solutions that can address these issues, without any modification to TCP. <br> <br> In network paths with significant available bandwidth, increasing the TCP window till observing loss can result in much lower throughput than the path's available bandwidth. We show that changes in TCP are {em not required} to utilize all the available bandwidth, and propose the application-layer SOcket Buffer Auto-Sizing (SOBAS) mechanism to achieve this goal. SOBAS relies on run-time estimation of the round trip time (RTT) and receive rate, and limits its socket buffer size when the receive rate approaches the path's available bandwidth. In a congested network, SOBAS does not limit its socket buffer size. Our experiment results show that SOBAS improves TCP throughput in uncongested network without hurting TCP performance in congested networks. <br> <br> Improper router buffer sizing can also result in poor TCP throughput. Previous research in router buffer sizing focused on network performance metrics such as link utilization or loss rate. Instead, we focus on the impact of buffer sizing on end-to-end TCP performance. We find that the router buffer size that optimizes TCP throughput is largely determined by the link's output to input capacity ratio. If that ratio is larger than one, the loss rate drops exponentially with the buffer size and the optimal buffer size is close to zero. Otherwise, if the output to input capacity ratio is lower than one, the loss rate follows a power-law reduction with the buffer size and significant buffering is needed. The amount of buffering required in this case depends on whether most flows end in the slow-start phase or in the congestion avoidance phase. <br> <br> TCP throughput also depends on whether the cross-traffic reduces its send rate upon congestion. We define this cross-traffic property as {em congestion responsiveness}. Since the majority of Internet traffic uses TCP, which reduces its send rate upon congestion, an aggregate of many TCP flows is believed to be congestion responsive. Here, we show that the congestion responsiveness of aggregate traffic also depends on the flow arrival process. If the flow arrival process follows an open-loop model, then even if the traffic consists exclusively of TCP transfers, the aggregate traffic can still be unresponsive to congestion. TCP flows that arrive in the network in a closed-loop manner are always congestion responsive, on the other hand. We also propose a scheme to estimate the fraction of traffic that follows the closed-loop model in a given link, and give practical guidelines to increase that fraction with simple application-layer modifications.

The purpose of this thesis is to develop some new algorithms based on optimization techniques for solving some problems in some areas of telecommunications and the Internet. There are two main parts to this thesis. In the first part we discuss optimization based stochastic and queueing models in telecommunications network corrective maintenance. In the second part we develop optimization based clustering (OBC) algorithms for network evolution and multicast routing. The most typical scenario encountered during mathematical optimization modelling in telecommunications, for example, is to minimize the cost of establishment and maintenance of the networks subject to the performance constraints of the networks and the reliability constraints of the networks as well. Most of these optimization problems are global optimization, that is, they have many local minima and most of these local minima do not provide any useful information for solving these problems. Therefore, the development of effective methods for solving such global optimization problems is important. To run the telecommunications networks with cost-effective network maintenance,we need to establish a practical maintenance model and optimize it. In the first part of the thesis, we solve a known stochastic programming maintenance optimization model with a direct method and then develop some new models. After that we introduce queue programming models in telecommunications network maintenance optimization. The ideas of profit, loss, and penalty will help telecommunications companies have a good view of their maintenance policies and help them improve their service. In the second part of this thesis we propose the use of optimization based clustering (OBC) algorithms to determine level-constrained hierarchical trees for network evolution and multicast routing. This problem is formulated as an optimization problem with a non-smooth, non-convex objective function. Different algorithms are examined for solving this problem. Results of numerical experiments using some artifiicial and real-world databases are reported. / Doctor of Philosophy

The purpose of this thesis is to develop some new algorithms based on optimization techniques for solving some problems in some areas of telecommunications and the Internet. There are two main parts to this thesis. In the first part we discuss optimization based stochastic and queueing models in telecommunications network corrective maintenance. In the second part we develop optimization based clustering (OBC) algorithms for network evolution and multicast routing. The most typical scenario encountered during mathematical optimization modelling in telecommunications, for example, is to minimize the cost of establishment and maintenance of the networks subject to the performance constraints of the networks and the reliability constraints of the networks as well. Most of these optimization problems are global optimization, that is, they have many local minima and most of these local minima do not provide any useful information for solving these problems. Therefore, the development of effective methods for solving such global optimization problems is important. To run the telecommunications networks with cost-effective network maintenance,we need to establish a practical maintenance model and optimize it. In the first part of the thesis, we solve a known stochastic programming maintenance optimization model with a direct method and then develop some new models. After that we introduce queue programming models in telecommunications network maintenance optimization. The ideas of profit, loss, and penalty will help telecommunications companies have a good view of their maintenance policies and help them improve their service. In the second part of this thesis we propose the use of optimization based clustering (OBC) algorithms to determine level-constrained hierarchical trees for network evolution and multicast routing. This problem is formulated as an optimization problem with a non-smooth, non-convex objective function. Different algorithms are examined for solving this problem. Results of numerical experiments using some artifiicial and real-world databases are reported. / Doctor of Philosophy

Thesis (M.Eng.(Electronic Engineering))--University of Pretoria, 2008. / Summary in English. Includes bibliographical references (leaves [92]-95).