Saturation routing for asynchronous transfer mode (ATM) networks

The main objective of this thesis is to show that saturation routing, often in the past considered inefficient, can in fact be a viable approach to use in many important applications and services over an Asynchronous Transfer Mode (ATM) network. For other applications and services, a hybrid approach (one that partially uses saturation routing) is presented. First, the minimum effects of saturation routing are demonstrated by showing that the ratio, defined as f, of routing overhead cells over information cells is small even for large networks. Second, modeling and simulation and M/D/l queuing analysis techniques are used to show that the overall effect on performance when using saturation routing is not significant over ATM networks. Then saturation routing ATM implementation is also provided, with important extensions to services such as multicast routing. After an analytical comparison, in terms of routing overhead, is made between Saturation Routing and the currently proposed Private Network-Network Interface (PNNI) procedure for ATM routing made by the ATM forum. This comparison is made for networks of different sizes (343node and 2401 -node networks) and different number of hierarchical levels (3 and 4 levels of hierarchy). The results show that the higher the number of levels of hierarchy and the farthest (in terms of hierarchical levels) the source and the destination nodes are from each other, the more advantageous saturation routing becomes. Finally, a set of measures of performance for use by saturation routing (or any routing algorithm), as metrics for routing path selection, is proposed. Among these measures, an innovative new measure of performance derived for measuring quality of service provided to Constant Bit Rate (CBR) users (e.g., such as voice and video users) called the Burst Voice Arrival Lag (BVAL) is described and derived

D3S: A Framework for Enabling Unmanned Aerial Vehicles as a Service

In this paper, we consider the use of UAVs to provide wireless connectivity services, for example after failures of wireless network components or to simply provide additional bandwidth on demand, and introduce the concept of UAVs as a service (UaaS). To facilitate UaaS, we introduce a novel framework, dubbed D3S, which consists of four phases: demand, decision, deployment, and service. The main objective of this framework is to develop efficient and realistic solutions to implement these four phases. The technical problems include determining the type and number of UAVs to be deployed, and also their final locations (e.g., hovering or on-ground), which is important for serving certain applications. These questions will be part of the decision phase. They also include trajectory planning of UAVs when they have to travel between charging stations and deployment locations and may have to do this several times. These questions will be part of the deployment phase. The service phase includes the implementation of the backbone communication and data routing between UAVs and between UAVs and ground control stations

Study of the Application of Neural Networks in Internet Traffic Engineering

In this study, we showed various approachs implemented in Artificial Neural Networks for network resources management and Internet congestion control. Through a training process, Neural Networks can determine nonlinear relationships in a data set by associating the corresponding outputs to input patterns. Therefore, the application of these networks to Traffic Engineering can help achieve its general objective: “intelligent” agents or systems capable of adapting dataflow according to available resources. In this article, we analyze the opportunity and feasibility to apply Artificial Neural Networks to a number of tasks related to Traffic Engineering. In previous sections, we present the basics of each one of these disciplines, which are associated to Artificial Intelligence and Computer Networks respectively

Control of transport dynamics in overlay networks

Transport control is an important factor in the performance of Internet protocols, particularly in the next generation network applications involving computational steering, interactive visualization, instrument control, and transfer of large data sets. The widely deployed Transport Control Protocol is inadequate for these tasks due to its performance drawbacks. The purpose of this dissertation is to conduct a rigorous analytical study on the design and performance of transport protocols, and systematically develop a new class of protocols to overcome the limitations of current methods. Various sources of randomness exist in network performance measurements due to the stochastic nature of network traffic. We propose a new class of transport protocols that explicitly accounts for the randomness based on dynamic stochastic approximation methods. These protocols use congestion window and idle time to dynamically control the source rate to achieve transport objectives. We conduct statistical analyses to determine the main effects of these two control parameters and their interaction effects. The application of stochastic approximation methods enables us to show the analytical stability of the transport protocols and avoid pre-selecting the flow and congestion control parameters. These new protocols are successfully applied to transport control for both goodput stabilization and maximization. The experimental results show the superior performance compared to current methods particularly for Internet applications. To effectively deploy these protocols over the Internet, we develop an overlay network, which resides at the application level to provide data transmission service using User Datagram Protocol. The overlay network, together with the new protocols based on User Datagram Protocol, provides an effective environment for implementing transport control using application-level modules. We also study problems in overlay networks such as path bandwidth estimation and multiple quickest path computation. In wireless networks, most packet losses are caused by physical signal losses and do not necessarily indicate network congestion. Furthermore, the physical link connectivity in ad-hoc networks deployed in unstructured areas is unpredictable. We develop the Connectivity-Through-Time protocols that exploit the node movements to deliver data under dynamic connectivity. We integrate this protocol into overlay networks and present experimental results using network to support a team of mobile robots

Congestion control mechanisms within MPLS networks

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TCP performance enhancement in wireless networks via adaptive congestion control and active queue management

The transmission control protocol (TCP) exhibits poor performance when used in error-prone wireless networks. Remedy to this problem has been an active research area. However, a widely accepted and adopted solution is yet to emerge. Difficulties of an acceptable solution lie in the areas of compatibility, scalability, computational complexity and the involvement of intermediate routers and switches. This dissertation rexriews the current start-of-the-art solutions to TCP performance enhancement, and pursues an end-to-end solution framework to the problem. The most noticeable cause of the performance degradation of TCP in wireless networks is the higher packet loss rate as compared to that in traditional wired networks. Packet loss type differentiation has been the focus of many proposed TCP performance enhancement schemes. Studies conduced by this dissertation research suggest that besides the standard TCP\u27s inability of discriminating congestion packet losses from losses related to wireless link errors, the standard TCP\u27s additive increase and multiplicative decrease (AIMD) congestion control algorithm itself needs to be redesigned to achieve better performance in wireless, and particularly, high-speed wireless networks. This dissertation proposes a simple, efficient, and effective end-to-end solution framework that enhances TCP\u27s performance through techniques of adaptive congestion control and active queue management. By end-to-end, it means a solution with no requirement of routers being wireless-aware or wireless-specific . TCP-Jersey has been introduced as an implementation of the proposed solution framework, and its performance metrics have been evaluated through extensive simulations. TCP-Jersey consists of an adaptive congestion control algorithm at the source by means of the source\u27s achievable rate estimation (ARE) —an adaptive filter of packet inter-arrival times, a congestion indication algorithm at the links (i.e., AQM) by means of packet marking, and a effective loss differentiation algorithm at the source by careful examination of the congestion marks carried by the duplicate acknowledgment packets (DUPACK). Several improvements to the proposed TCP-Jersey have been investigated, including a more robust ARE algorithm, a less computationally intensive threshold marking algorithm as the AQM link algorithm, a more stable congestion indication function based on virtual capacity at the link, and performance results have been presented and analyzed via extensive simulations of various network configurations. Stability analysis of the proposed ARE-based additive increase and adaptive decrease (AJAD) congestion control algorithm has been conducted and the analytical results have been verified by simulations. Performance of TCP-Jersey has been compared to that of a perfect , but not practical, TCP scheme, and encouraging results have been observed. Finally the framework of the TCP-Jersey\u27s source algorithm has been extended and generalized for rate-based congestion control, as opposed to TCP\u27s window-based congestion control, to provide a design platform for applications, such as real-time multimedia, that do not use TCP as transport protocol yet do need to control network congestion as well as combat packet losses in wireless networks. In conclusion, the framework architecture presented in this dissertation that combines the adaptive congestion control and active queue management in solving the TCP performance degradation problem in wireless networks has been shown as a promising answer to the problem due to its simplistic design philosophy complete compatibility with the current TCP/IP and AQM practice, end-to-end architecture for scalability, and the high effectiveness and low computational overhead. The proposed implementation of the solution framework, namely TCP-Jersey is a modification of the standard TCP protocol rather than a completely new design of the transport protocol. It is an end-to-end approach to address the performance degradation problem since it does not require split mode connection establishment and maintenance using special wireless-aware software agents at the routers. The proposed solution also differs from other solutions that rely on the link layer error notifications for packet loss differentiation. The proposed solution is also unique among other proposed end-to-end solutions in that it differentiates packet losses attributed to wireless link errors from congestion induced packet losses directly from the explicit congestion indication marks in the DUPACK packets, rather than inferring the loss type based on packet delay or delay jitter as in many other proposed solutions; nor by undergoing a computationally expensive off-line training of a classification model (e.g., HMM), or a Bayesian estimation/detection process that requires estimations of a priori loss probability distributions of different loss types. The proposed solution is also scalable and fully compatible to the current practice in Internet congestion control and queue management, but with an additional function of loss type differentiation that effectively enhances TCP\u27s performance over error-prone wireless networks. Limitations of the proposed solution architecture and areas for future researches are also addressed

ROUTING IN A NETWORK WITH UNRELIABLE COMPONENTS

A new approach to the joint selection of primary and secondary routes in a network with unreliable components is presented. The mathematical model captures the changes in the operational characteristics of the network when it adapts to failures. Lagrangean relaxation and subgradient optimization techniques are used to obtain good heuristic solutions to the problem, as well as lower bounds to be used as benchmarks against which the quality of the solution is assessed. Results of numerical experiments are reported, and directions for further enhancements of the model are discussed.Information Systems Working Papers Serie

Enhanced Reserve Procurement Policies for Power Systems with Increasing Penetration Levels of Stochastic Resources

abstract: The uncertainty and variability associated with stochastic resources, such as wind and solar, coupled with the stringent reliability requirements and constantly changing system operating conditions (e.g., generator and transmission outages) introduce new challenges to power systems. Contemporary approaches to model reserve requirements within the conventional security-constrained unit commitment (SCUC) models may not be satisfactory with increasing penetration levels of stochastic resources; such conventional models pro-cure reserves in accordance with deterministic criteria whose deliverability, in the event of an uncertain realization, is not guaranteed. Smart, well-designed reserve policies are needed to assist system operators in maintaining reliability at least cost. Contemporary market models do not satisfy the minimum stipulated N-1 mandate for generator contingencies adequately. This research enhances the traditional market practices to handle generator contingencies more appropriately. In addition, this research employs stochastic optimization that leverages statistical information of an ensemble of uncertain scenarios and data analytics-based algorithms to design and develop cohesive reserve policies. The proposed approaches modify the classical SCUC problem to include reserve policies that aim to preemptively anticipate post-contingency congestion patterns and account for resource uncertainty, simultaneously. The hypothesis is to integrate data-mining, reserve requirement determination, and stochastic optimization in a holistic manner without compromising on efficiency, performance, and scalability. The enhanced reserve procurement policies use contingency-based response sets and post-contingency transmission constraints to appropriately predict the influence of recourse actions, i.e., nodal reserve deployment, on critical transmission elements. This research improves the conventional deterministic models, including reserve scheduling decisions, and facilitates the transition to stochastic models by addressing the reserve allocation issue. The performance of the enhanced SCUC model is compared against con-temporary deterministic models and a stochastic unit commitment model. Numerical results are based on the IEEE 118-bus and the 2383-bus Polish test systems. Test results illustrate that the proposed reserve models consistently outperform the benchmark reserve policies by improving the market efficiency and enhancing the reliability of the market solution at reduced costs while maintaining scalability and market transparency. The proposed approaches require fewer ISO discretionary adjustments and can be employed by present-day solvers with minimal disruption to existing market procedures.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201