skip to main content

Search for: All records

Award ID contains: 2004323

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. The management of drinking water quality is critical to public health and can benefit from techniques and technologies that support near real-time forecasting of lake and reservoir conditions. The cyberinfrastructure (CI) needed to support forecasting has to overcome multiple challenges, which include: 1) deploying sensors at the reservoir requires the CI to extend to the network’s edge and accommodate devices with constrained network and power; 2) different lakes need different sensor modalities, deployments, and calibrations; hence, the CI needs to be flexible and customizable to accommodate various deployments; and 3) the CI requires to be accessible and usable to various stakeholders (water managers, reservoir operators, and researchers) without barriers to entry. This paper describes the CI underlying FLARE (Forecasting Lake And Reservoir Ecosystems), a novel system co-designed in an interdisciplinary manner between CI and domain scientists to address the above challenges. FLARE integrates R packages that implement the core numerical forecasting (including lake process modeling and data assimilation) with containers, overlay virtual networks, object storage, versioned storage, and event-driven Function-as-a-Service (FaaS) serverless execution. It is a flexible forecasting system that can be deployed in different modalities, including the Manual Mode suitable for end-users’ personal computers and the Workflow Mode idealmore »for cloud deployment. The paper reports on experimental data and lessons learned from the operational deployment of FLARE in a drinking water supply (Falling Creek Reservoir in Vinton, Virginia, USA). Experiments with a FLARE deployment quantify its edge-to-cloud virtual network performance and serverless execution in OpenWhisk deployments on both XSEDE-Jetstream and the IBM Cloud Functions FaaS system.« less
  2. Edge and fog computing encompass a variety of technologies that are poised to enable new applications across the Internet that support data capture, storage, processing, and communication across the networking continuum. These environments pose new challenges to the design and implementation of networks-as membership can be dynamic and devices are heterogeneous, widely distributed geographically, and in proximity to end-users, as is the case with mobile and Internet-of-Things (IoT) devices. We present a demonstration of EdgeVPN.io (Evio for short), an open-source programmable, software-defined network that addresses challenges in the deployment of virtual networks spanning distributed edge and cloud resources, in particular highlighting its use in support of the Kubernetes container orchestration middleware. The demo highlights a deployment of unmodified Kubernetes middleware across a virtual cluster comprising virtual machines deployed both in cloud providers, and in distinct networks at the edge-where all nodes are assigned private IP addresses and subject to different NAT (Network Address Translation) middleboxes, connected through an Evio virtual network. The demo includes an overview of the configuration of Kubernetes and Evio nodes and the deployment of Docker-based container pods, highlighting the seamless connectivity for TCP/IP applications deployed on the pods.