Understanding the Earth's climate system and how it might be changing is a preeminent scientific challenge. Global climate models are used to simulate past, present, and future climates, and experiments are executed continuously on an array of distributed supercomputers. The resulting data archive, spread over several sites, currently contains upwards of 100 TB of simulation data and is growing rapidly. Looking toward mid-decade and beyond, we must anticipate and prepare for distributed climate research data holdings of many petabytes. The Earth System Grid (ESG) is a collaborative interdisciplinary project aimed at addressing the challenge of enabling management, discovery, access, and analysis of these critically important datasets in a distributed and heterogeneous computational environment. The problem is fundamentally a Grid problem. Building upon the Globus toolkit and a variety of other technologies, ESG is developing an environment that addresses authentication, authorization for data access, large-scale data transport and management, services and abstractions for high-performance remote data access, mechanisms for scalable data replication, cataloging with rich semantic and syntactic information, data discovery, distributed monitoring, and Web-based portals for using the system.

By leveraging current technologies to manage distributed climate data in a unified virtual environment, the Earth System Grid (ESG) project is promoting data sharing between international research centers and diverse users. In transforming these data into a collaborative community resource, ESG is changing the way global climate research is conducted. Since ESG's production beginnings in 2004, its most notable accomplishment was to efficiently store and distribute climate simulation data of some 20 global coupled ocean-atmosphere models to the scores of scientific contributors to the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC); the IPCC collective scientific achievement was recognized by the award of a 2007 Nobel Peace Prize. Other international climate stakeholders such as the North American Regional Climate Change Assessment Program (NARCCAP) and the developers of the Community Climate System Model (CCSM) and of the Climate Science Computational End Station (CC... By leveraging current technologies to manage distributed climate data in a unified virtual environment, the Earth System Grid (ESG) project is promoting data sharing between international research centers and diverse users. In transforming these data into a collaborative community resource, ESG is changing the way global climate research is conducted. Since ESG's production beginnings in 2004, its most notable accomplishment was to efficiently store and distribute climate simulation data of some 20 global coupled ocean-atmosphere models to the scores of scientific contributors to the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC); the IPCC collective scientific achievement was recognized by the award of a 2007 Nobel Peace Prize. Other international climate stakeholders such as the North American Regional Climate Change Assessment Program (NARCCAP) and the developers of the Community Climate System Model (CCSM) and of the Climate Science Computational End Station (CC...

Wi-Fi has become the wireless networking standard that allows short- to medium-range device to connect without wires. For the last 20 year, the Wi-Fi technology has so pervasive that most devices in use today are mobile and connect to the internet through Wi-Fi. Unlike wired network, a wireless network lacks a clear boundary, which leads to significant Wi-Fi network security concerns, especially because the current security measures are prone to several types of intrusion. To address this problem, machine learning and deep learning methods have been successfully developed to identify network attacks. However, collecting data to develop models is expensive and raises privacy concerns. The goal of this paper is to evaluate a federated learning approach that would alleviate such privacy concerns. This initial work on intrusion detection is performed in a simulated environment. Once proven feasible, this process would allow edge devices to collaboratively update global anomaly detection models, without sharing sensitive training data. On a set of tests with the AWID intrusion detection data set, we show that our federated approach is effective in terms of classification accuracy, computation cost, as well as communication cost.

Many scientific applications need support from a communication infrastructure that provides predictable performance, which requires effective algorithms for bandwidth reservations. Network reservation systems such as ESnet's OSCARS, establish guaranteed bandwidth of secure virtual circuits for a certain bandwidth and length of time. However, users currently cannot inquire about bandwidth availability, nor have alternative suggestions when reservation requests fail. In general, the number of reservation options is exponential with the number of nodes n, and current reservation commitments. We present a novel approach for path finding in time-dependent networks taking advantage of user-provided parameters of total volume and time constraints, which produces options for earliest completion and shortest duration. The theoretical complexity is only O(n²r²) in the worst-case, where r is the number of reservations in the desired time interval. We have implemented our algorithm and developed efficient methodologies for incorporation into network reservation frameworks. Performance measurements confirm the theoretical predictions.

Traffic flow prediction is a key component of an intelligent transportation system. Accurate traffic flow prediction provides a foundation for other tasks, such as signal coordination and travel time forecasting. There are many known methods in literature for the short-term traffic flow prediction problem, but their efficacy depends heavily on the traffic characteristics. It is difficult, if not impossible, to pick a single method that works well over time. In this paper, we present an automated framework to address this practical issue. Instead of selecting a single method, we combine predictions from multiple methods to generate a consensus traffic flow prediction. We propose an ensemble learning model that exploits the temporal characteristics of the data, and balances the accuracy of individual models and their mutual dependence through a covariance-regularizer. We additionally use a pruning scheme to remove anomalous individual predictions. We apply our proposed model to multi-step-ahead arterial roadway flow prediction. In tests, our method consistently outperforms recently published ensemble prediction methods based on ridge regression and lasso. Our method also produces steady results even when the standalone models and other ensemble methods make wildly exaggerated predictions.