Simple analytical estimation of router bypass effectiveness

Large scale IP networks contain thousands of network devices such as routers and switches. Massive amounts of logging data is generated by these devices. Analysing this data is both a challenge and an opportunity for finding network problems. Moreover, large IP networks contain devices from different vendors, so it is important to build a system which can work with network devices of different brands. In this study we describe a distributed architecture which can retrieve, store, and process massive amounts of network logging data in real time. Using this architecture we also build a basic anomaly detection system. The system statistically models cumulative counts of logs for different event types for all the devices in the network. The statistical approach lets the system to detect deviations from the normal behaviour without consulting expert knowledge. Our evaluations show that the system effectively handles massive amounts of data and detects anomalies.

The expansion in computer networks leads the traffic matrix estimation problem to be an essential component in managing the networks. In this paper, we have estimated the large IP network traffic matrix (TM) using a Levenberg-Marquardt Neural Network. Traffic matrix estimation is generally a complicated task for a large scale IP network, therefore, we involved neural networks for the intelligent and accurate estimation of it. The performance of proposed Neural Network has been verified using Abilene dataset available publicly for researchers and it has been observed that our used algorithm gives promising results when it comes to traffic matrix estimation.

An increasingly diverse set of applications, such as Internet games, streaming videos, e-commerce, online banking, and even mission-critical emergency call services, all relies on IP networks. In such an environment, best-effort service is no longer acceptable. This requires a transformation in network management from detecting and replacing individual faulty network elements to managing the end-to-end service quality as a whole. In this paper, we describe the design and development of a Generic Root Cause Analysis platform (G-RCA) for service quality management (SQM) in large IP networks. G-RCA contains a comprehensive service dependency model that incorporates topological and cross-layer relationships, protocol interactions, and control plane dependencies. G-RCA abstracts the root cause analysis process into signature identification for symptom and diagnostic events, temporal and spatial event correlation, and reasoning and inference logic. G-RCA provides a flexible rule specification language that allows operators to quickly customize G-RCA and provide different root cause analysis tools as new problems need to be investigated. G-RCA is also integrated with data trending, manual data exploration, and statistical correlation mining capabilities. G-RCA has proven to be a highly effective SQM platform in several different applications, and we present results regarding BGP flaps, PIM flaps in Multicast VPN service, and end-to-end throughput degradation in content delivery network (CDN) service.

Revisiting simplified model of a single-well push–pull test for estimating regional flow velocity

This paper proposes the TPED MBAC (two-phase edge-to-edge distributed measurement based admission control) mechanism that provides the guarantee of user QoS requirements and the fairness of blocking probability between flows traversing different path lengths in large IP networks. The proposed mechanism has two provisioning phases, the quantitative provisioning phase and the qualitative provisioning phase, in admission control and performs WRR scheduling based on the hop counter for probing packet transmission. Additionally, the proposed mechanism uses the optimized probing packet rate instead of the peak rate. For the performance evaluation of the proposed mechanism, we present simulation results in terms of the fairness of blocking probability and the loss probability using the ns-2 simulator.

Traffic matrices are required inputs for many IP network management tasks, such as capacity planning, traffic engineering, and network reliability analysis. However, it is difficult to measure these matrices directly in large operational IP networks, so there has been recent interest in inferring traffic matrices from link measurements and other more easily measured data. Typically, this inference problem is ill-posed, as it involves significantly more unknowns than data. Experience in many scientific and engineering fields has shown that it is essential to approach such ill-posed problems via regularization. This paper presents a new approach to traffic matrix estimation using a regularization based on entropy penalization. Our solution chooses the traffic matrix consistent with the measured data that is information-theoretically closest to a model in which source/destination pairs are stochastically independent. It applies to both point-to-point and point-to-multipoint traffic matrix estimation. We use fast algorithms based on modern convex optimization theory to solve for our traffic matrices. We evaluate our algorithm with real backbone traffic and routing data, and demonstrate that it is fast, accurate, robust, and flexible.

We consider the Kuramoto-Sivashinsky (KS) equation in finite domains of the form $[-L,L]$. Our main result provides effective new estimates for higher Sobolev norms of the solutions in terms of powers of $L$ for the one-dimentional differentiated KS. We illustrate our method on a simpler model, namely the regularized Burger's equation. The underlying idea in this result is that a priori control of the $L^2$ norm is enough in order to conclude higher order regularity and in fact, it allows one to get good estimates on the high-frequency tails of the solution.

In this paper, simple regression estimates and factor-based models are utilised to produce forecasts for Bahrain quarterly gross domestic product growth. Using simulated out-of-sample experiments, we assess and compare the performance of the simple regression estimates, which exploit the available information on selected indicator variables, with factor-based estimates. These estimates use up to 65 variables to obtain new factors that embody most of the potential information and handle it in a systematic way following the Stock–Watson approach. Additionally, we compare the performance of the nowcast factor MIDAS with the quarterly factor (static-SW) models based on time-aggregated data, which neglect the most recent information. Our empirical findings can be summarised as follows. First, using more information does not help to produce more accurate results. Preselected indicator variables can clearly improve the forecast performance in comparison with the use of large dataset. Second, quarterly factor models are in general outperformed by the nowcast factor models that directly relate low-frequency data to those of high frequency. Third, the best forecasting performance can be reached using simple regression estimates with a handful of variables. However, it fails in density forecast evaluation tests. Thus, the alternative factor-MIDAS model is considered as an optimal model that passes all the performance evaluation tests. Fourth, concerning the difference between MIDAS projection methods, the results indicate that MIDAS with exponential distributed lag functions outperforms the MIDAS with unrestricted lag polynomials. The best performing projection based on the number of factors is the model with three factors. They can pick up the rapid switch in the utility of the indicators automatically. Finally, although the most accurate Flash estimates are obtained at 84 days, nowcasting using industrial production into a bridge equation witnessed insignificant loss in accuracy at 54 days only.

We study several dynamical properties of a recently proposed implementation of the quantum transverse-field Ising chain in the framework of circuit quantum electrodynamics (QED). Particular emphasis is placed on the effects of disorder on the nonequilibrium behavior of the system. We show that small amounts of fabrication-induced disorder in the system parameters do not jeopardize the observation of previously predicted phenomena. Based on a numerical extraction of the mean free path of a wave packet in the system, we also provide a simple quantitative estimate for certain disorder effects on the nonequilibrium dynamics of the circuit QED quantum simulator. We discuss the transition from weak to strong disorder, characterized by the onset of Anderson localization of the system's wave functions, and the qualitatively different dynamics it leads to.

The use of state estimation techniques offers a means of inferring rotor effective wind speed from standard measurements of wind turbines. Typical wind speed estimators rely upon a pre‐computed quasi‐steady aerodynamic mapping, which describes the relationship between pitch angle and tip‐speed ratio and the power coefficient. In practice, the static mapping does not capture the influence of turbine structural dynamics and atmospheric turbulence, inevitably resulting in poor performance of the wind speed estimation. In addition, the turbine aerodynamic properties might not be easily accessible. Thus, this paper presents a rotor effective wind speed estimation method that obviates the requirement for prior knowledge of turbine power coefficients. Specifically, the proposed method exploits a simple actuator disc model, where the aerodynamic power and thrust coefficients can be characterized in terms of axial induction factors. Based on this insight and standard turbine measurements, real‐time estimation of rotor effective wind speed and axial induction factors can then be achieved using a simplified turbine drive‐train model and an extended Kalman filter. In addition, the actuator disc model can be updated easily over time by calibrating solely two correction factors. Thus, the proposed algorithm presents an alternative for estimating the rotor effective wind speed, which is valuable for numerous applications, for example, LiDAR‐assisted control and coherence studies.

The recovery of offshore wind farm wakes in the German Bight was analyzed by a unique in situ data set, measured on‐board the research aircraft Dornier Do‐128 during the WIPAFF project in 2016 and 2017. These observations were used to validate a simple analytical wake recovery model in five case studies. The observed recovery rates were compared with the results of the mesoscale Weather Research and Forecasting (WRF) model. The airborne data show that the wake recovery can be described by an exponential function as expected by the analytical model and strengthens the hypothesis that the vertical downward moment flux has an important influence on the wake recovery. However, the predicted wake recovery rates (by the analytical model) do not always fully agree with the observations. Although, as a first‐order approximation, the model seems to perform well, further optimization has to be implemented to account for wind park layout, turbine induced turbulence, and horizontal momentum flux. WRF simulations reveal an exponential recovery, although the mesoscale model does not reproduce the correct atmospheric conditions for most of the cases. Therefore, the wake recovery rates estimated by WRF disagree with the measured data in most of the studied cases.

Simplified structural behaviours of post-tensioned inter-module connection for modular buildings

Effect of chamber pressure on particle velocities in low pressure plasma spray deposition

A simple model is presented to predict the long-term behavior of heavy metals in agricultural soils. The analytical solutions to the model differ in the assumed relationships between the solubility of heavy metals and their accumulated contents in soils. Four cases are distinguished: (1) precipitation and dissolution reactions determine the solubility of heavy metals in soil, (2 ) ion exchange or linear adsorption determine the solubility of heavy metals, (3) solution concen­ trations of heavy metals are related to their accumulated soil contents through Langmuir-or Freundlich-type adsorption equations, and (4) part of the heavy metals in an adsorbed phase is transformed into unavailable (irreversibly adsorbed) form. As an example, the model is applied to assess the long-term behavior of cadmium in agricultural soils in The Netherlands.

Getting from “just‐in‐case” to “just‐in‐time”: Insights from a simple model