Tiresias: Online Anomaly Detection for Hierarchical Operational Network Data

Operational network data, management data such as customer care call logs and equipment system logs, is a very important source of information for network operators to detect problems in their networks. Unfortunately, there is lack of efficient tools to automatically track and detect anomalous events on operational data, causing ISP operators to rely on manual inspection of this data. While anomaly detection has been widely studied in the context of network data, operational data presents several new challenges, including the volatility and sparseness of data, and the need to perform fast detection (complicating application of schemes that require offline processing or large/stable data sets to converge). To address these challenges, we propose Tiresias, an automated approach to locating anomalous events on hierarchical operational data. Tiresias leverages the hierarchical structure of operational data to identify high-impact aggregates (e.g., locations in the network, failure modes) likely to be associated with anomalous events. To accommodate different kinds of operational network data, Tiresias consists of an online detection algorithm with low time and space complexity, while preserving high detection accuracy. We present results from two case studies using operational data collected at a large commercial IP network operated by a Tier-1 ISP: customer care call logs and set-top box crash logs. By comparing with a reference set verified by the ISP's operational group, we validate that Tiresias can achieve >94% accuracy in locating anomalies. Tiresias also discovered several previously unknown anomalies in the ISP's customer care cases, demonstrating its effectiveness

Large Magnetoresistance in Compensated Semimetals TaAs2_2 and NbAs2_2

We report large magnetoresistance (MR) at low temperatures in single-crystalline nonmagnetic compounds TaAs$_2$ and NbAs$_2$. Both compounds exhibit parabolic-field-dependent MR larger than $5\times10^3$ in a magnetic field of 9 Tesla at 2 K. The MR starts to deviate from parabolic dependence above 10 T and intends to be saturated in 45 T for TaAs$_2$ at 4.2 K. The Hall resistance measurements and band structural calculations reveal their compensated semimetal characteristics. The large MR at low temperatures is ascribed to a resonance effect of the balanced electrons and holes with large mobilities. We also discuss the relation of the MR and samples' quality for TaAs$_2$ and other semimetals. We found that the magnitudes of MR are strongly dependent on the samples' quality for different compounds.Comment: 26 pages, 11 figures, 2 table

Cascading failures in coupled networks with both inner-dependency and inter-dependency links

We study the percolation in coupled networks with both inner-dependency and inter-dependency links, where the inner- and inter-dependency links represent the dependencies between nodes in the same or different networks, respectively. We find that when most of dependency links are inner- or inter-ones, the coupled networks system is fragile and makes a discontinuous percolation transition. However, when the numbers of two types of dependency links are close to each other, the system is robust and makes a continuous percolation transition. This indicates that the high density of dependency links could not always lead to a discontinuous percolation transition as the previous studies. More interestingly, although the robustness of the system can be optimized by adjusting the ratio of the two types of dependency links, there exists a critical average degree of the networks for coupled random networks, below which the crossover of the two types of percolation transitions disappears, and the system will always demonstrate a discontinuous percolation transition. We also develop an approach to analyze this model, which is agreement with the simulation results well.Comment: 9 pages, 4 figure

Robustness analysis of signaling transduction networks based on Monte-Carlo method

The dynamic behaviors of cell system were deep ly affected by structural complexity of cell signal transduction networks and uncertainty of kinetics parameters. How to quantitatively determinate the relation between system behaviors and parameters variations was an important p roblem of systems biology. In order to study robustness of NF - κB signal transduction networks, the parameters of system model were assigned to subject to stochastic distributions. Then, robustness of system output signal NF - κBn with respect to 64 parameters variations and amp litude variation of step input signal IKK was studied by means of Monte - Carlo method. The simulation results demonstrate that the oscillation behavior of system output signal NF - κBn is closely relative to 6 key rate constantswhose robustness isweak, and the amp litude variation of step input signal IKKmakes a great impact on the oscillation behavior of system output

On Power Law Scaling Dynamics for Time-fractional Phase Field Models during Coarsening

In this paper, we study the phase field models with fractional-order in time. The phase field models have been widely used to study coarsening dynamics of material systems with microstructures. It is known that phase field models are usually derived from energy variation so that they obey some energy dissipation laws intrinsically. Recently, many works have been published on investigating fractional-order phase field models, but little is known of the corresponding energy dissipation laws. We focus on the time-fractional phase field models and report that the effective free energy and roughness obey a universal power-law scaling dynamics during coarsening. Mainly, the effective free energy and roughness in the time-fractional phase field models scale by following a similar power law as the integer phase field models, where the power is linearly proportional to the fractional order. This universal scaling law is verified numerically against several phase field models, including the Cahn-Hilliard equations with different variable mobilities and molecular beam epitaxy models. This new finding sheds light on potential applications of time fractional phase field models in studying coarsening dynamics and crystal growths

A new fractal viscoelastic element: Promise and applications to Maxwell-rheological model

This paper proposes a fractal viscoelastic element via He?s fractal derivative, its properties are analyzed in details by the two-scale transform for the first time. The element is used to establish a fractal Maxwell-rheological model, which unifies the fractal creep equation and relaxation equation, and includes the classic elastic model and the classical Maxwell-rheological model as two special cases. This paper sheds a bright light on viscoelasticity, and the model can find wide applications in rock mechanics, plastic mechanics, and non-continuum mechanics