Internet routing paths stability model and relation to forwarding paths

Analysis of real datasets to characterize the local stability properties of the Internet routing paths suggests that extending the route selection criteria to account for such property would not increase the routing path length. Nevertheless, even if selecting a more stable routing path could be considered as valuable from a routing perspective, it does not necessarily imply that the associated forwarding path would be more stable. Hence, if the dynamics of the Internet routing and forwarding system show different properties, then one can not straightforwardly derive the one from the other. If this assumption is verified, then the relationship between the stability of the forwarding path (followed by the traffic) and the corresponding routing path as selected by the path-vector routing algorithm requires further characterization. For this purpose, we locally relate, i.e., at the router level, the stability properties of routing path with the corresponding forwarding path. The proposed stability model and measurement results verify this assumption and show that, although the main cause of instability results from the forwarding plane, a second order effect relates forwarding and routing path instability events. This observation provides the first indication that differential stability can safely be taken into account as part of the route selection process

Routing Regardless of Network Stability

We examine the effectiveness of packet routing in this model for the broad class next-hop preferences with filtering. Here each node v has a filtering list D(v) consisting of nodes it does not want its packets to route through. Acceptable paths (those that avoid nodes in the filtering list) are ranked according to the next-hop, that is, the neighbour of v that the path begins with. On the negative side, we present a strong inapproximability result. For filtering lists of cardinality at most one, given a network in which an equilibrium is guaranteed to exist, it is NP-hard to approximate the maximum number of packets that can be routed to within a factor of O(n^{1-\epsilon}), for any constant \epsilon >0. On the positive side, we give algorithms to show that in two fundamental cases every packet will eventually route with probability one. The first case is when each node's filtering list contains only itself, that is, D(v)={v}. Moreover, with positive probability every packet will be routed before the control plane reaches an equilibrium. The second case is when all the filtering lists are empty, that is, $\mathcal{D}(v)=\emptyset$. Thus, with probability one packets will route even when the nodes don't care if their packets cycle! Furthermore, with probability one every packet will route even when the control plane has em no equilibrium at all.Comment: ESA 201

Vintage Capital and the Dynamics of the AK Model

This paper analyzes the equilibrium dynamics of an AK-type endogenous growth model with vintage capital. The inclusion of vintage capital leads to oscillatory dynamics governed by replacement echoes, which additionally influence the intercept of the balanced growth path. These features, which are in sharp contrast to those from the standard AK model, can contribute to explaining the short-run deviations observed between investment and growth rates time series. To characterize the convergence properties and the dynamics of the model we develop analytical and numerical methods that should be of interest for the general resolution of endogenous growth models with vintage capital.

Efficient Bayesian hierarchical functional data analysis with basis function approximations using Gaussian-Wishart processes

Functional data are defined as realizations of random functions (mostly smooth functions) varying over a continuum, which are usually collected with measurement errors on discretized grids. In order to accurately smooth noisy functional observations and deal with the issue of high-dimensional observation grids, we propose a novel Bayesian method based on the Bayesian hierarchical model with a Gaussian-Wishart process prior and basis function representations. We first derive an induced model for the basis-function coefficients of the functional data, and then use this model to conduct posterior inference through Markov chain Monte Carlo. Compared to the standard Bayesian inference that suffers serious computational burden and unstableness for analyzing high-dimensional functional data, our method greatly improves the computational scalability and stability, while inheriting the advantage of simultaneously smoothing raw observations and estimating the mean-covariance functions in a nonparametric way. In addition, our method can naturally handle functional data observed on random or uncommon grids. Simulation and real studies demonstrate that our method produces similar results as the standard Bayesian inference with low-dimensional common grids, while efficiently smoothing and estimating functional data with random and high-dimensional observation grids where the standard Bayesian inference fails. In conclusion, our method can efficiently smooth and estimate high-dimensional functional data, providing one way to resolve the curse of dimensionality for Bayesian functional data analysis with Gaussian-Wishart processes.Comment: Under revie

Is History a Blessing or a Curse? International Borrowing without Commitment, Leapfrogging and Growth Reversals

We develop a simple open-economy AK model with collateral constraints that accounts for growth-reversal episodes, during which countries face abrupt changes in their growth rate that lead to either growth miracles or growth disasters. Absent commitment to investment by the borrowing country, imperfect contract enforcement leads to an informational lag such that the debt contracted upon today depends upon the past stock of capital. The no-commitment delay originates a history effect by which the richer a country has been in the past, the more it can borrow today. For (arbitrarily) small deviations from perfect contract enforcement, the history effect offsets the growth benefits from international borrowing and dampens growth, and it leads to leapfrogging in long-run levels. When large enough, the history effect originates growth reversals and we connect the latter to leapfrogging. Finally, we argue that the model accords with the reported evidence on growth disasters and growth accelerations. We also provide examples showing that leapfrogging and growth reversals may coexist, so that currently poor but fast-growing countries experiencing sharp growth reversals may end up, in the long-run, significantly richer than currently rich but declining countries.Growth Reversals; Leapfrogging; International Borrowing; Open Economies