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Abstract: This paper studies the optimization of malicious software removal or patch deployment processes across multiple networks. The well-known classical epidemic model is adapted to model malware propagation in this multi-network framework. The trade-off between the infection spread and the patching costs is captured in a cost function, leading to an optimal control problem. In the single network case the optimal feedback controller is found by solving an associated Hamilton–Jacobi–Bellman equation. This control law is numerically compared to the proportional response strategy typically assumed by the epidemic model. In the higher dimensional multiple-networks case, the system is linearized to derive feedback controllers using pole-placement, linear quadratic regulator (LQR) optimal control, and optimal control, where the measurement errors in the number of infected clients are explicitly modeled. The resulting patching strategies are analyzed numerically and their results are compared. [Copyright &y& Elsevier]

This paper initiates a study toward developing and applying randomized algorithms for stability of high-speed communication networks. The focus is on congestion and delay-based flow controllers for sources, which are "utility maximizers" for individual users. First, we introduce a nonlinear algorithm for such source flow controllers, which uses as feedback aggregate congestion and delay information from bottleneck nodes of the network, and depends on a number of parameters, among which are link capacities, user preference for utility, and pricing. We then linearize this nonlinear model around its unique equilibrium point and perform a robustness analysis for a special symmetric case with a single bottleneck node. The "symmetry" here captures the scenario when certain utility and pricing parameters are the same across all active users, for which we derive closed-form necessary and sufficient conditions for stability and robustness under parameter variations. In addition, the ranges of values for the utility and pricing parameters for which stability is guaranteed are computed exactly. These results also admit counterparts for the case when the pricing parameters vary across users, but the utility parameter values are still the same. In the general nonsymmetric case, when closed-form derivation is not possible, we construct specific randomized algorithms which provide a probabilistic estimate of the local stability of the network. In particular, we use Monte Carlo as well as quasi-Monte Carlo techniques for the linearized model. The results obtained provide a complete analysis of congestion con- trol algorithms for internet style networks with a single bottleneck node as well as for networks with general random topologies. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we investigate stability, bifurcation and oscillations arising in a single-link communication network model with a large number of heterogeneous users adopting a Transmission Control Protocol (TCP)-like rate control scheme with an Active Queue Management (AQM) router. In the system considered, different user delays are known and fixed but taken from a given distribution. It is shown that for any given distribution of delays, there exists a critical amount of feedback (due to AQM) at which the equilibrium loses stability and a limit cycling solution develops via a Hopf bifurcation. The nature (criticality) of the bifurcation is investigated with the aid of Lyapunov–Schmidt perturbation method. The results of the analysis are numerically verified and provide valuable insights into dynamics of the AQM control system. [Copyright &y& Elsevier]