Quo Vadis - a framework for intelligent routing in large communication networks

Modern communication networks contain hundreds if not thousands of interconnected nodes. Traffic management mechanisms must be able to support a cost-effective, responsive, flexible, robust, customer-oriented high speed communication environment while minimizing the overhead associated with management functions. Conventional traffic management mechanisms for routing and congestion control algorithms entail tremendous resource overhead in storage and update of network state information;Quo Vadis is an evolving framework for intelligent traffic management in very large communication networks. It is designed to exploit topological properties of large networks as well as their spatio-temporal dynamics to optimize multiple performance criteria through cooperation among nodes in the network. It employs a distributed representation of network state information using local load measurements supplemented by a less precise global summary. Routing decisions in Quo Vadis are based on parameterized heuristics designed to optimize various performance metrics in an anticipatory or pro-active as well as compensatory or reactive mode and to minimize the overhead associated with traffic management;The complexity of modern networks in terms of the number of entities, their interaction, and the resulting dynamics make an analytical study often impossible. Hence, we have designed and implemented an object oriented simulation toolbox to facilitate the experimental studies of Quo Vadis. Our efforts to design such a simulation environment were driven by the need to evaluate heuristic routing strategies and knowledge representation as employed by Quo Vadis. The results of simulation experiments within a grid network clearly demonstrate the ability of Quo Vadis to avoid congestion and minimize message delay under a variety of network load conditions;In order to provide a theoretical framework for the design and analytical study of decision mechanisms as employed by Quo Vadis, we draw upon concepts from the field of utility theory. Based on the concept of reward and cost incurred by messages in the network, utility functions which bias routing decisions so as to yield routes that circumvent congested areas have been designed. The existence of utility functions which yield minimum cost routes in uniform cost networks with a single congested node has been proven rigorously

Modeling immune response and its effect on infectious disease outbreak dynamics

This article presents a model that incorporates individuals' immune responses to further examine the role of the collective immune response of individuals in a population during an infectious outbreak

Modeling and Simulating Computer Networks Using Formalized Data Flow Diagrams

Formalized Data Flow Diagrams (FDFD\u27s) provide simple and natural abstractions for specifying network behavior. We have augmented FDFD\u27s by providing (i) a notion of asynchronous timing of events, (ii) a convenient mechanism for describing node classes , and (iii) features for specifying network architecture. These extensions will facilitate the modeling of a large computer network (using the langauge NET-SPECS) and the direct generation of a simulator of that network

Temporal analysis of infectious diseases: influenza

A Bayesian network is developed to embed the probabilistic reasoning dependencies of the demographics on the incidence of infectious diseases. Influenza epidemics occur every year in both hemispheres during the winter. The Bayesian learning paradigm is used to create synthetic data sets that simulate an outbreak of influenza for a geographic area. The Bayesian prior and posterior probabilities can be altered to represent an outbreak for various demographics in different ideographic regions. Epidemic curves are generated, via time series analysis of the data sets, for the temporal flow of influenza on different variants of the demographics. The analysis of the demographic-based epidemic curves facilitates in the identification of the risk levels among the different demographic sections. Spread vaccination lowers the impact of the epidemic, depending on the efficacy of the vaccine. Our model is equipped to analyze the effects of spread vaccination and design vaccination strategies, that optimize the use of public health resources, by identifying high-risk demographic groups. Our results show that application of the vaccine in the order of risk levels will further lower the epidemic impact as compared to uniform spread vaccination

MODELING INFECTIOUS DISEASES USING GLOBAL STOCHASTIC CELLULAR AUTOMATA

Susceptibles-infectives-removals (SIR) and its derivatives are the classic mathematical models for the study of infectious diseases in epidemiology. In order to model and simulate epidemics of an infectious disease, we use cellular automata (CA). The simplifying assumptions of SIR and naive CA limit their applicability to the real world characteristics. A global stochastic cellular automata paradigm (GSCA) is proposed, which incorporates geographic and demographic based interactions. The interaction measure between the cells is a function of population density and Euclidean distance, and has been extended to include geographic, demographic and migratory constraints. The progression of diseases using traditional CA and classic SIR are analyzed, and similar behavior to the SIR model is exhibited by GSCA, using the geographic information systems (GIS) gravity model for interactions. The limitations of the SIR and naive CA models of homogeneous population with uniform mixing are addressed by the GSCA model. The GSCA model is oriented to heterogeneous population, and can incorporate interactions based on geography, demography, environment and migration patterns. The progression of diseases can be modeled at higher levels of fidelity using the GSCA model, and facilitates optimal deployment of public health resources for prevention, control and surveillance of infectious diseases. </jats:p

Computational epidemiology: Bayesian disease surveillance

Disease monitoring plays a crucial role in the implementation of public health measures. The demographic profiles of the people and the disease prevalence in a geographic region are analyzed for inter-causal relationships. Bayesian analysis of the data identifies the pertinent characteristics of the disease under study. The vital components of control and prevention of the disease spread are identified by Bayesian learning for the efficient utilization of the limited public health resources. Bayesian computing, layered with epidemiological expertise, provides the public health personnel to utilize their available resources optimally to minimize the prevalence of the disease. Bayesian analysis is implemented using synthetic data for two different demographic and geographic scenarios for pneumonia and influenza, that exhibit similar symptoms. The analysis infers results on the effects of the demographic parameters, namely ethnicity, gender, age, and income levels, on the evidence of the prevalence of the diseases. Bayesian learning brings in the probabilistic reasoning capabilities to port the inferences derived from one region to another

Dynamic resource management in QoS controlled networks

This paper addresses the problem of resource fragmentation (RF) in QoS controllednetworks. Resources are said to be fragmented when they are available in non-contiguousblocks and hence cannot be utilized by incoming calls with high resource demands. This paper shows the effect of resource fragmentation on QoS controlled networks and presents the Dynamic Resource Redistribution (DRR) algorithm to counteract RF. The DRR algorithm reduces the effects of RF by attempting to redistribute resources in different paths to make resources to incoming calls. A variety of simulation experiments were conducted to study the performance of the DRR algorithm on different network topologies with varying traffic characteristics. The DRR algorithm, when used, increased the number of calls accommodated in the network as well as the overall resource allocation in the network. © Springer Science + Business Media, LLC 2006

A Framework to Simulate and Visualize Epidemics

This poster was featured at the 2013 Perot Museum of Nature and Science's 'Social Science' exhibit. The poster discusses a framework to simulate and visualize epidemics