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

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