Service quality measurements for IPv6 inter-networks

Measurement-based performance evaluation of network traffic is becoming very important, especially for networks trying to provide differentiated levels of service quality to the different application flows. The non-identical response of flows to the different types of network-imposed performance degradation raises the need for ubiquitous measurement mechanisms, able to measure numerous performance properties, and being equally applicable to different applications and transports. This paper presents a new measurement mechanism, facilitated by the steady introduction of IPv6 in network nodes and hosts, which exploits native features of the protocol to provide support for performance measurements at the network (IP) layer. IPv6 Extension Headers have been used to carry the triggers involving the measurement activity and the measurement data in-line with the payload data itself, providing a high level of probability that the behaviour of the real user traffic flows is observed. End-to-end one-way delay, jitter, loss, and throughput have been measured for applications operating on top of both reliable and unreliable transports, over different-capacity IPv6 network configurations. We conclude that this technique could form the basis for future Internet measurements that can be dynamically deployed where and when required in a multi-service IP environment

Kinematics of the X-shaped Milky Way Bulge: Expectations from a Self-consistent N-body Model

We explore the kinematics (both the radial velocity and the proper motion) of the vertical X-shaped feature in the Milky Way with an N-body bar/bulge model. From the solar perspective, the distance distribution of particles is double-peaked in fields passing through the X-shape. The separation and amplitude ratio between the two peaks qualitatively match the observed trends towards the Galactic bulge. We confirm clear signatures of cylindrical rotation in the pattern of mean radial velocity across the bar/bulge region. We also find possible imprints of coherent orbital motion inside the bar structure in the radial velocity distribution along l=0 degree, where the near and far sides of the bar/bulge show excesses of approaching and receding particles. The coherent orbital motion is also reflected in the slight displacement of the zero-velocity-line in the mean radial velocity, and the displacement of the maximum/minimum in the mean longitudinal proper motion across the bulge region. We find some degree of anisotropy in the stellar velocity within the X-shape, but the underlying orbital family of the X-shape cannot be clearly distinguished. Two potential applications of the X-shape in previous literature are tested, i.e., bulge rotation and Galactic center measurements. We find that the proper motion difference between the two sides of the X-shape can be used to estimate the mean azimuthal streaming motion of the bulge, but not the pattern speed of the bar. We also demonstrate that the Galactic center can be located with the X-shape, but the accuracy depends on the fitting scheme, the number of fields, and their latitudinal coverage.Comment: Minor changes to match the ApJ accepted version; 17 pages; emulateapj format. The electronic tables of our model result are available upon reques

The Milky Way bar/bulge in proper motions: a 3D view from VIRAC & Gaia

© 2019 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society.We have derived absolute proper motions of the entire Galactic bulge region from VIRAC and Gaia. We present these as both integrated on-sky maps and, after isolating standard candle red clump (RC) stars, as a function of distance using RC magnitude as a proxy. These data provide a new global, 3-dimensional view of the Milky Way barred bulge kinematics. We find a gradient in the mean longitudinal proper motion, $\mu_l$, between the different sides of the bar, which is sensitive to the bar pattern speed. The split RC has distinct proper motions and is colder than other stars at similar distance. The proper motion correlation map has a quadrupole pattern in all magnitude slices showing no evidence for a separate, more axisymmetric inner bulge component. The line-of-sight integrated kinematic maps show a high central velocity dispersion surrounded by a more asymmetric dispersion profile. $\sigma_{\mu_l} / \sigma_{\mu_b}$ is smallest, $\sim1.1$, near the minor axis and reaches $\sim1.4$ near the disc plane. The integrated $$ pattern signals a superposition of bar rotation and internal streaming motion, with the near part shrinking in latitude and the far part expanding. To understand and interpret these remarkable data, we compare to a made-to-measure barred dynamical model, folding in the VIRAC selection function to construct mock maps. We find that our model of the barred bulge, with a pattern speed of 37.5 $\mathrm{km \, s^{-1} \, kpc^{-1}}$, is able to reproduce all observed features impressively well. Dynamical models like this will be key to unlocking the full potential of these data.Peer reviewe

SOTXTSTREAM: Density-based self-organizing clustering of text streams

A streaming data clustering algorithm is presented building upon the density-based selforganizing stream clustering algorithm SOSTREAM. Many density-based clustering algorithms are limited by their inability to identify clusters with heterogeneous density. SOSTREAM addresses this limitation through the use of local (nearest neighbor-based) density determinations. Additionally, many stream clustering algorithms use a two-phase clustering approach. In the first phase, a micro-clustering solution is maintained online, while in the second phase, the micro-clustering solution is clustered offline to produce a macro solution. By performing self-organization techniques on micro-clusters in the online phase, SOSTREAM is able to maintain a macro clustering solution in a single phase. Leveraging concepts from SOSTREAM, a new density-based self-organizing text stream clustering algorithm, SOTXTSTREAM, is presented that addresses several shortcomings of SOSTREAM. Gains in clustering performance of this new algorithm are demonstrated on several real-world text stream datasets

A new density estimation neural network to detect abnormal condition in streaming data

Along with the development of monitoring technologies, numerous measured data pour into monitoring system and form the high-volume and open-ended data stream. Usually, abnormal condition of monitored system can be characterized by the density variation of measured data stream. However, traditional density estimation methods can not dynamically track density variation of data stream due to the limitation of processing time and computation memory. In this paper, we propose a new density estimation neural network to continuously estimate the density of streaming data in a time-based sliding window. The network has a feedforward structure composed of discretization, input and summation layer. In the discretization layer, value range of data stream is discretized to network nodes with equal intervals. Measured data in the predefined time window are pushed into input layer and updated with the window sliding. In summation layer, the activation results between input neurons and discretization neurons are summed up and multiplied by a weight factor. The network outputs the kernel density estimators of sliding segment in data stream and achieves a one-pass estimation algorithm consuming constant computation memory. By subnet separation and local activation, computation load of the network is significantly reduced to catch up the pace of data stream. The nonlinear statistics, quantile and entropy, which can be consecutively figured out with the density estimators output by the density estimation neural network, are calculated as condition indictors to track the density variation of data stream. The proposed method is evaluated by a simulated data stream consisting of two mixing distribution data sets and a pressure data stream measured from a centrifugal compressor respectively. Results show that the underlying anomalies are successfully detected