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Munther A. Dahleh - One of the best experts on this subject based on the ideXlab platform.

  • Robust network routing under cascading failures
    53rd IEEE Conference on Decision and Control, 2014
    Co-Authors: Ketan Savla, Giacomo Como, Munther A. Dahleh
    Abstract:

    We propose a dynamical model for cascading failures in single-commodity network flows. In the proposed model, the network state consists of flows and activation status of the links. Network dynamics is determined by a, possibly state-dependent and adversarial, Disturbance Process that reduces flow capacity on the links, and routing policies at the nodes that have access to the network state, but are oblivious to the presence of Disturbance. Under the proposed dynamics, a link becomes irreversibly inactive either due to overload condition on itself or on all of its immediate downstream links. The coupling between link activation and flow dynamics implies that links to become inactive successively are not necessarily adjacent to each other, and hence the pattern of cascading failure under our model is qualitatively different than standard cascade models. The magnitude of a Disturbance Process is defined as the sum of cumulative capacity reductions across time and links of the network, and the margin of resilience of the network is defined as the infimum over the magnitude of all Disturbance Processes under which the links at the origin node become inactive. We propose an algorithm to compute an upper bound on the margin of resilience for the setting where the routing policy only has access to information about the local state of the network. For the limiting case when the routing policies update their action as fast as network dynamics, we give sufficient conditions on network parameters under which the upper bound is tight under an appropriate routing policy.

  • Robust Network Routing under Cascading Failures
    IEEE Transactions on Network Science and Engineering, 2014
    Co-Authors: Ketan Savla, Giacomo Como, Munther A. Dahleh
    Abstract:

    We propose a dynamical model for cascading failures in single-commodity network flows. In the proposed model, the network state consists of flows and activation status of the links. Network dynamics is determined by a, possibly state-dependent and adversarial, Disturbance Process that reduces flow capacity on the links, and routing policies at the nodes that have access to the network state, but are oblivious to the presence of Disturbance. Under the proposed dynamics, a link becomes irreversibly inactive either due to overload condition on itself or on all of its immediate downstream links. The coupling between link activation and flow dynamics implies that links to become inactive successively are not necessarily adjacent to each other, and hence the pattern of cascading failure under our model is qualitatively different than standard cascade models. The magnitude of a Disturbance Process is defined as the sum of cumulative capacity reductions across time and links of the network, and the margin of resilience of the network is defined as the infimum over the magnitude of all Disturbance Processes under which the links at the origin node become inactive. We propose an algorithm to compute an upper bound on the margin of resilience for the setting where the routing policy only has access to information about the local state of the network. For the limiting case when the routing policies update their action as fast as network dynamics, we identify sufficient conditions on network parameters under which the upper bound is tight under an appropriate routing policy. Our analysis relies on making connections between network parameters and monotonicity in network state evolution under proposed dynamics.

  • CDC - Robust Network Routing under Cascading Failures
    IEEE Transactions on Network Science and Engineering, 2014
    Co-Authors: Ketan Savla, Giacomo Como, Munther A. Dahleh
    Abstract:

    We propose a dynamical model for cascading failures in single-commodity network flows. In the proposed model, the network state consists of flows and activation status of the links. Network dynamics is determined by a, possibly state-dependent and adversarial, Disturbance Process that reduces flow capacity on the links, and routing policies at the nodes that have access to the network state, but are oblivious to the presence of Disturbance. Under the proposed dynamics, a link becomes irreversibly inactive either due to overload condition on itself or on all of its immediate downstream links. The coupling between link activation and flow dynamics implies that links to become inactive successively are not necessarily adjacent to each other, and hence the pattern of cascading failure under our model is qualitatively different than standard cascade models. The magnitude of a Disturbance Process is defined as the sum of cumulative capacity reductions across time and links of the network, and the margin of resilience of the network is defined as the infimum over the magnitude of all Disturbance Processes under which the links at the origin node become inactive. We propose an algorithm to compute an upper bound on the margin of resilience for the setting where the routing policy only has access to information about the local state of the network. For the limiting case when the routing policies update their action as fast as network dynamics, we identify sufficient conditions on network parameters under which the upper bound is tight under an appropriate routing policy. Our analysis relies on making connections between network parameters and monotonicity in network state evolution under proposed dynamics.

Ketan Savla - One of the best experts on this subject based on the ideXlab platform.

  • Robust network routing under cascading failures
    53rd IEEE Conference on Decision and Control, 2014
    Co-Authors: Ketan Savla, Giacomo Como, Munther A. Dahleh
    Abstract:

    We propose a dynamical model for cascading failures in single-commodity network flows. In the proposed model, the network state consists of flows and activation status of the links. Network dynamics is determined by a, possibly state-dependent and adversarial, Disturbance Process that reduces flow capacity on the links, and routing policies at the nodes that have access to the network state, but are oblivious to the presence of Disturbance. Under the proposed dynamics, a link becomes irreversibly inactive either due to overload condition on itself or on all of its immediate downstream links. The coupling between link activation and flow dynamics implies that links to become inactive successively are not necessarily adjacent to each other, and hence the pattern of cascading failure under our model is qualitatively different than standard cascade models. The magnitude of a Disturbance Process is defined as the sum of cumulative capacity reductions across time and links of the network, and the margin of resilience of the network is defined as the infimum over the magnitude of all Disturbance Processes under which the links at the origin node become inactive. We propose an algorithm to compute an upper bound on the margin of resilience for the setting where the routing policy only has access to information about the local state of the network. For the limiting case when the routing policies update their action as fast as network dynamics, we give sufficient conditions on network parameters under which the upper bound is tight under an appropriate routing policy.

  • Robust Network Routing under Cascading Failures
    IEEE Transactions on Network Science and Engineering, 2014
    Co-Authors: Ketan Savla, Giacomo Como, Munther A. Dahleh
    Abstract:

    We propose a dynamical model for cascading failures in single-commodity network flows. In the proposed model, the network state consists of flows and activation status of the links. Network dynamics is determined by a, possibly state-dependent and adversarial, Disturbance Process that reduces flow capacity on the links, and routing policies at the nodes that have access to the network state, but are oblivious to the presence of Disturbance. Under the proposed dynamics, a link becomes irreversibly inactive either due to overload condition on itself or on all of its immediate downstream links. The coupling between link activation and flow dynamics implies that links to become inactive successively are not necessarily adjacent to each other, and hence the pattern of cascading failure under our model is qualitatively different than standard cascade models. The magnitude of a Disturbance Process is defined as the sum of cumulative capacity reductions across time and links of the network, and the margin of resilience of the network is defined as the infimum over the magnitude of all Disturbance Processes under which the links at the origin node become inactive. We propose an algorithm to compute an upper bound on the margin of resilience for the setting where the routing policy only has access to information about the local state of the network. For the limiting case when the routing policies update their action as fast as network dynamics, we identify sufficient conditions on network parameters under which the upper bound is tight under an appropriate routing policy. Our analysis relies on making connections between network parameters and monotonicity in network state evolution under proposed dynamics.

  • CDC - Robust Network Routing under Cascading Failures
    IEEE Transactions on Network Science and Engineering, 2014
    Co-Authors: Ketan Savla, Giacomo Como, Munther A. Dahleh
    Abstract:

    We propose a dynamical model for cascading failures in single-commodity network flows. In the proposed model, the network state consists of flows and activation status of the links. Network dynamics is determined by a, possibly state-dependent and adversarial, Disturbance Process that reduces flow capacity on the links, and routing policies at the nodes that have access to the network state, but are oblivious to the presence of Disturbance. Under the proposed dynamics, a link becomes irreversibly inactive either due to overload condition on itself or on all of its immediate downstream links. The coupling between link activation and flow dynamics implies that links to become inactive successively are not necessarily adjacent to each other, and hence the pattern of cascading failure under our model is qualitatively different than standard cascade models. The magnitude of a Disturbance Process is defined as the sum of cumulative capacity reductions across time and links of the network, and the margin of resilience of the network is defined as the infimum over the magnitude of all Disturbance Processes under which the links at the origin node become inactive. We propose an algorithm to compute an upper bound on the margin of resilience for the setting where the routing policy only has access to information about the local state of the network. For the limiting case when the routing policies update their action as fast as network dynamics, we identify sufficient conditions on network parameters under which the upper bound is tight under an appropriate routing policy. Our analysis relies on making connections between network parameters and monotonicity in network state evolution under proposed dynamics.

Giacomo Como - One of the best experts on this subject based on the ideXlab platform.

  • Robust network routing under cascading failures
    53rd IEEE Conference on Decision and Control, 2014
    Co-Authors: Ketan Savla, Giacomo Como, Munther A. Dahleh
    Abstract:

    We propose a dynamical model for cascading failures in single-commodity network flows. In the proposed model, the network state consists of flows and activation status of the links. Network dynamics is determined by a, possibly state-dependent and adversarial, Disturbance Process that reduces flow capacity on the links, and routing policies at the nodes that have access to the network state, but are oblivious to the presence of Disturbance. Under the proposed dynamics, a link becomes irreversibly inactive either due to overload condition on itself or on all of its immediate downstream links. The coupling between link activation and flow dynamics implies that links to become inactive successively are not necessarily adjacent to each other, and hence the pattern of cascading failure under our model is qualitatively different than standard cascade models. The magnitude of a Disturbance Process is defined as the sum of cumulative capacity reductions across time and links of the network, and the margin of resilience of the network is defined as the infimum over the magnitude of all Disturbance Processes under which the links at the origin node become inactive. We propose an algorithm to compute an upper bound on the margin of resilience for the setting where the routing policy only has access to information about the local state of the network. For the limiting case when the routing policies update their action as fast as network dynamics, we give sufficient conditions on network parameters under which the upper bound is tight under an appropriate routing policy.

  • Robust Network Routing under Cascading Failures
    IEEE Transactions on Network Science and Engineering, 2014
    Co-Authors: Ketan Savla, Giacomo Como, Munther A. Dahleh
    Abstract:

    We propose a dynamical model for cascading failures in single-commodity network flows. In the proposed model, the network state consists of flows and activation status of the links. Network dynamics is determined by a, possibly state-dependent and adversarial, Disturbance Process that reduces flow capacity on the links, and routing policies at the nodes that have access to the network state, but are oblivious to the presence of Disturbance. Under the proposed dynamics, a link becomes irreversibly inactive either due to overload condition on itself or on all of its immediate downstream links. The coupling between link activation and flow dynamics implies that links to become inactive successively are not necessarily adjacent to each other, and hence the pattern of cascading failure under our model is qualitatively different than standard cascade models. The magnitude of a Disturbance Process is defined as the sum of cumulative capacity reductions across time and links of the network, and the margin of resilience of the network is defined as the infimum over the magnitude of all Disturbance Processes under which the links at the origin node become inactive. We propose an algorithm to compute an upper bound on the margin of resilience for the setting where the routing policy only has access to information about the local state of the network. For the limiting case when the routing policies update their action as fast as network dynamics, we identify sufficient conditions on network parameters under which the upper bound is tight under an appropriate routing policy. Our analysis relies on making connections between network parameters and monotonicity in network state evolution under proposed dynamics.

  • CDC - Robust Network Routing under Cascading Failures
    IEEE Transactions on Network Science and Engineering, 2014
    Co-Authors: Ketan Savla, Giacomo Como, Munther A. Dahleh
    Abstract:

    We propose a dynamical model for cascading failures in single-commodity network flows. In the proposed model, the network state consists of flows and activation status of the links. Network dynamics is determined by a, possibly state-dependent and adversarial, Disturbance Process that reduces flow capacity on the links, and routing policies at the nodes that have access to the network state, but are oblivious to the presence of Disturbance. Under the proposed dynamics, a link becomes irreversibly inactive either due to overload condition on itself or on all of its immediate downstream links. The coupling between link activation and flow dynamics implies that links to become inactive successively are not necessarily adjacent to each other, and hence the pattern of cascading failure under our model is qualitatively different than standard cascade models. The magnitude of a Disturbance Process is defined as the sum of cumulative capacity reductions across time and links of the network, and the margin of resilience of the network is defined as the infimum over the magnitude of all Disturbance Processes under which the links at the origin node become inactive. We propose an algorithm to compute an upper bound on the margin of resilience for the setting where the routing policy only has access to information about the local state of the network. For the limiting case when the routing policies update their action as fast as network dynamics, we identify sufficient conditions on network parameters under which the upper bound is tight under an appropriate routing policy. Our analysis relies on making connections between network parameters and monotonicity in network state evolution under proposed dynamics.

A.b. Haurie - One of the best experts on this subject based on the ideXlab platform.

  • Control of singularly perturbed hybrid stochastic systems
    IEEE Transactions on Automatic Control, 2001
    Co-Authors: J.a. Filar, V. Gaitsgory, A.b. Haurie
    Abstract:

    We study a class of optimal stochastic control problems involving two different time scales. The fast mode of the system is represented by deterministic state equations whereas the slow mode of the system corresponds to a jump Disturbance Process. Under a fundamental "ergodicity" property for a class of "infinitesimal control systems" associated with the fast mode, we show that there exists a limit problem which provides a good approximation to the optimal control of the perturbed system. Both the finite- and infinite-discounted horizon cases are considered. We show how an approximate optimal control law can be constructed from the solution of the limit control problem. In the particular case where the infinitesimal control systems possess the so-called turnpike property, i.e., characterized by the existence of global attractors, the limit control problem can be given an interpretation related to a decomposition approach.

  • Control of singularly perturbed hybrid stochastic systems
    Proceedings of 35th IEEE Conference on Decision and Control, 1996
    Co-Authors: J.a. Filar, V. Gaitsgory, A.b. Haurie
    Abstract:

    We study a class of optimal stochastic control problems involving two different time scales. The fast mode of the system is represented by deterministic state equations whereas the slow mode of the system corresponds to a jump Disturbance Process. Under a fundamental "ergodicity" property for a class of "infinitesimal control systems" associated with the fast mode, we show that there exists a limit problem which provides a good approximation to the optimal control of the perturbed system. Both the finite and infinite discounted horizon cases are considered. We show how an approximate optimal control law can be constructed from the solution of the limit control problem. In the particular case where the infinitesimal control systems possess the so-called turnpike property, i.e. are characterized by the existence of global attractors, the limit control problem can be given an interpretation related to a decomposition approach. Due to the constraints on page numbers all results are presented without proofs.

Peter Egger - One of the best experts on this subject based on the ideXlab platform.

  • Estimation and testing of higher-order spatial autoregressive panel data error component models
    Journal of Geographical Systems, 2013
    Co-Authors: Harald Badinger, Peter Egger
    Abstract:

    This paper develops an estimator for higher-order spatial autoregressive panel data error component models with spatial autoregressive Disturbances, SARAR( R , S ). We derive the moment conditions and optimal weighting matrix without distributional assumptions for a generalized moments (GM) estimation procedure of the spatial autoregressive parameters of the Disturbance Process and define a generalized two-stage least squares estimator for the regression parameters of the model. We prove consistency of the proposed estimators, derive their joint asymptotic distribution, and provide Monte Carlo evidence on their small sample performance.

  • Estimation of spatial autoregressive M-way error component panel data models
    The Annals of Regional Science, 2011
    Co-Authors: Harald Badinger, Peter Egger
    Abstract:

    This paper considers a first order spatial autoregressive panel data model with first-order spatial autoregressive Disturbances, SARAR(1,1), and M -dimensional error components. We derive generalized moments (GM) estimators for the spatial autoregressive parameter of the Disturbance Process and the variances of the error components and define a feasible generalized two stages least squares (FG2SLS) estimator for the regression parameters of the model. Finally, we prove consistency and derive the joint asymptotic distribution of the GM and FG2SLS estimators, enabling specification tests and a proper estimation of multi-way error component models with cross-sectionally dependent observations.

  • Estimation of Higher-Order Spatial Autoregressive Panel Data Error Component Models
    2009
    Co-Authors: Harald Badinger, Peter Egger
    Abstract:

    This paper develops an estimator for higher-order spatial autoregressive panel data error component models with spatial autoregressive Disturbances, SARAR(R,S). We derive the moment conditions and optimal weighting matrix without distributional assumptions for a generalized moments (GM) estimation procedure of the spatial autoregressive parameters of the Disturbance Process and define a generalized two-stages least squares estimator for the regression parameters of the model. We prove consistency of the proposed estimators, derive their joint asymptotic distribution, and provide Monte Carlo evidence on their small sample performance.