A comparison of Poisson and uniform sampling for active measurements

Copyright © 2006 IEEEActive probes of network performance represent samples of the underlying performance of a system. Some effort has gone into considering appropriate sampling patterns for such probes, i.e., there has been significant discussion of the importance of sampling using a Poisson process to avoid biases introduced by synchronization of system and measurements. However, there are unanswered questions about whether Poisson probing has costs in terms of sampling efficiency, and there is some misinformation about what types of inferences are possible with different probe patterns. This paper provides a quantitative comparison of two different sampling methods. This paper also shows that the irregularity in probing patterns is useful not just in avoiding synchronization, but also in determining frequency-domain properties of a system. This paper provides a firm basis for practitioners or researchers for making decisions about the type of sampling they should use in a particular applications, along with methods for the analysis of their outputs.Matthew Rougha

On the correlation of internet packet losses

Copyright © 2008 IEEEIn this paper we analyze more than 100 hours of packet traces from Planet-Lab measurements to study the correlation of Internet packet losses. We first apply statistical tests to identify the correlation timescale of the binary loss data. We find that in half of the traces packet losses are far from independent. More significantly, the correlation timescale of packet losses is correlated with the network load. We then examine the loss runs and the success runs of packets. The loss runs are typically short, regardless of the network load. We find that the success runs in the majority of our traces are also uncorrelated. Furthermore, their correlation timescale also does not depend on the network load. All of these results show that the impact of network load on the correlation of packet losses is nontrivial and that loss runs and success runs are better modeled as being independent than the binary losses themselves. © 2008 IEEE.Hung X. Nguyen and Matthew Rougha

Efficacy of Feedforward and LSTM Neural Networks at Predicting and Gap Filling Coastal Ocean Timeseries: Oxygen, Nutrients, and Temperature

Ocean data timeseries are vital for a diverse range of stakeholders (ranging from government, to industry, to academia) to underpin research, support decision making, and identify environmental change. However, continuous monitoring and observation of ocean variables is difficult and expensive. Moreover, since oceans are vast, observations are typically sparse in spatial and temporal resolution. In addition, the hostile ocean environment creates challenges for collecting and maintaining data sets, such as instrument malfunctions and servicing, often resulting in temporal gaps of varying lengths. Neural networks (NN) have proven effective in many diverse big data applications, but few oceanographic applications have been tested using modern frameworks and architectures. Therefore, here we demonstrate a “proof of concept” neural network application using a popular “off-the-shelf” framework called “TensorFlow” to predict subsurface ocean variables including dissolved oxygen and nutrient (nitrate, phosphate, and silicate) concentrations, and temperature timeseries and show how these models can be used successfully for gap filling data products. We achieved a final prediction accuracy of over 96% for oxygen and temperature, and mean squared errors (MSE) of 2.63, 0.0099, and 0.78, for nitrates, phosphates, and silicates, respectively. The temperature gap-filling was done with an innovative contextual Long Short-Term Memory (LSTM) NN that uses data before and after the gap as separate feature variables. We also demonstrate the application of a novel dropout based approach to approximate the Bayesian uncertainty of these temperature predictions. This Bayesian uncertainty is represented in the form of 100 monte carlo dropout estimates of the two longest gaps in the temperature timeseries from a model with 25% dropout in the input and recurrent LSTM connections. Throughout the study, we present the NN training process including the tuning of the large number of NN hyperparameters which could pose as a barrier to uptake among researchers and other oceanographic data users. Our models can be scaled up and applied operationally to provide consistent, gap-free data to all data users, thus encouraging data uptake for data-based decision making

Internet scalability: properties and evolution

Copyright © 2008 IEEEMatthew Roughan; Steve Uhlig; Walter Willinge

Dynamics of Interannual Eddy Kinetic Energy Modulations in a Western Boundary Current

Among Western Boundary Currents, the East Australian Current (EAC) has a more energetic eddy field relative to its mean flow, however, the relationship between upstream transport and downstream eddy kinetic energy (EKE) is still unclear. We investigate the modulation of downstream EKE in the EAC's typical separation region (Tasman EKE Box) (33. (Formula presented.) S–36. (Formula presented.) S) based on a long-term (22-year), high-resolution (2.5–6 km) model simulation and satellite altimeter observations from 1994 to 2016. Our results show that the poleward EAC transport at (Formula presented.) S leads the EKE in the Tasman EKE Box by 93–118 days. Barotropic instabilities are the primary source of EKE, and they control EKE variability in the EAC system. Anticyclonic eddies shed from the EAC dominate from (Formula presented.) S– (Formula presented.) S during high-EKE periods, but in low-EKE periods anticyclonic eddies penetrate even further south by (Formula presented.)

The Rate of Coastal Temperature Rise Adjacent to a Warming Western Boundary Current is Nonuniform with Latitude

Western boundary currents (WBCs) have intensified and become more eddying in recent decades due to the spin-up of the ocean gyres, resulting in warmer open ocean temperatures. However, relatively little is known of how WBC intensification will affect temperatures in adjacent continental shelf waters where societal impact is greatest. We use the well-observed East Australian Current (EAC) to investigate WBC warming impacts on shelf waters and show that temperature increases are nonuniform in shelf waters along the latitudinal extent of the EAC. Shelf waters poleward of 32°S are warming more than twice as fast as those equatorward of 32°S. We show that nonuniform shelf temperature trends are driven by an increase in lateral heat advection poleward of the WBC separation, along Australia's most populous coastline. The large-scale nature of the process indicates that this is applicable to WBCs broadly, with far-reaching biological implications

Variability and Drivers of Ocean Temperature Extremes in a Warming Western Boundary Current

Western boundary current (WBC) extensions such as the East Australian Current (EAC) southern extension are warming 2-3 times faster than the global average. However, there are nuances in the spatial and temporal variability of the warming that are not well resolved in climate models. In addition, the physical drivers of ocean heat content (OHC) extremes are not well understood. Here, using a high-resolution ocean model run for multiple decades, we show nonuniform warming trends in OHC in the EAC, with strong positive trends in the southern extension region (∼368-388S) but negative OHC trends equatorward of 338S. The OHC variability in the EAC is associated with the formation of anticyclonic eddies, which is modulated by transport ∼880 km upstream (EAC mode) and the westward propagation of Rossby waves (eddy mode). Diagnosing the drivers of temperature extremes has implications for predictability both in the EAC and inWBCs more broadly, where ocean warming is already having considerable ecological impacts

Estimating point-to-point and point-to-multipoint traffic matrices: An information-theoretic approach

© 2005 IEEE.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.Yin Zhang, Member, Matthew Roughan, Carsten Lund, and David L. Donoh

Node localisation in wireless ad hoc networks

Wireless ad hoc networks often require a method for estimating their nodes' locations. Typically this is achieved by the use of pair-wise measurements between nodes and their neighbours, where a number of nodes already accurately know their location and the remaining nodes must calculate theirs using these known locations. Typically, a minimum mean square estimate (MMSE), or a maximum likelihood estimate (MLE) is used to generate the unknown node locations, making use of range estimates derived from measurements between the nodes. In this paper we investigate the efficacy of using radio frequency, received signal strength (RSS) measurements for the accurate location of the transmitting nodes over long ranges. We show with signal strength measurements from three or more wireless probes in noisy propagation conditions, that by using a weighted MMSE approach we can obtain significant improvements in the variance of the location estimate over both the standard MMSE and MLE approaches.Jon Arnold, Nigel Bean, Miro Kraetzl, Matthew Rougha