Komal Thareja

In distributed systems and networking research, translating theoretical ideas into real experiments remains difficult. Researchers spend much of their time on implementation details, including infrastructure provisioning, software configuration, and data collection, rather than the scientific questions that motivate the work. AI can help automate experiment execution so researchers can focus on research questions and interpretation. We present LoomAI, an open-source, browser-based graphical interface for the international FABRIC testbed that takes a step toward this vision. LoomAI enables AI-assisted design, deployment, and autonomous execution of experiments on FABRIC and other testbeds. It introduces Weaves, packaged experiments that provision slices, autonomously manage execution, collect and analyze data, and present results in an embedded JupyterLab. LoomAI integrates with the FABRIC Artifact Manager for community sharing and supports both commercial AI tools such as Claude Code and open-weight models hosted on FABRIC and National Research Platform (NRP) GPUs at no cost, keeping researcher data off commercial infrastructure. LoomAI is FABRIC’s effort toward AI-augmented research experimentation.

Real-time network monitoring is essential for ensuring reliable and efficient scientific data transmission and operational continuity during oceanographic research expeditions. This article describes the deployment and operational experience of a perfSONAR-based network performance monitoring system aboard the research vessel Sikuliaq from the U.S. Academic Research Fleet during a cyberinfrastructure training expedition in April 2025. We describe the system architecture, deployment strategies, and integration with contextual environmental data collected using the CORIOLIX data presence framework. We analyze the empirical results from ship-to-shore network tests to assess system performance under varying maritime conditions during the cruise. The article further discusses practical challenges encountered, system adaptations implemented, and future directions to advance resilient cyberinfrastructure support for mobile research platforms, such as research vessels.

Over the past few years, due to the boom of advances in image processing, edge computing, and wireless networking, unpiloted aerial vehicles, often referred to as drones, have become an important enabler to support a wide variety of scientific applications, ranging from environmental monitoring, disaster response, and wildfire monitoring to the survey of archaeological sites. In this article, we present the FlyNet platform, which extends an existing workflow management system to support and manage scientific workflows. FlyNet enables automated resource allocation, workflow instrumentation, and network service support to support researchers in their goal to analyze data for new scientific discoveries. In addition, FlyNet provides network services management to support quality of service for efficient data transport between edge devices, edge servers, and the cloud.

A majority of today’s cloud services are independently operated by individual cloud service providers. In this approach, the locations of cloud resources are strictly constrained by the distribution of cloud service providers’ sites. As the popularity and scale of cloud services increase, we believe this traditional paradigm is about to change toward further federated services, a.k.a., multi-cloud, due to the improved performance, reduced cost of compute, storage and network resources, as well as increased user demands. In this paper, we present COMET, a lightweight, distributed storage system for managing metadata on large scale, federated cloud infrastructure providers, end users, and their applications (e.g. HTCondor Cluster or Hadoop Cluster). We showcase use case from NSF’s, Chameleon, ExoGENI and JetStream research cloud testbeds to show the effectiveness of COMET design and deployment.

Computational science today depends on complex, data-intensive applications operating on datasets from a variety of scientific instruments. A major challenge is the integration of data into the scientist's workflow. Recent advances in dynamic, networked cloud resources provide the building blocks to construct reconfigurable, end-to-end infrastructure that can increase scientific productivity. However, applications have not adequately taken advantage of these advanced capabilities. In this work, we have developed a novel network-centric platform that enables high-performance, adaptive data flows and coordinated access to distributed cloud resources and data repositories for atmospheric scientists. We demonstrate the effectiveness of our approach by evaluating time-critical, adaptive weather sensing workflows, which utilize advanced networked infrastructure to ingest live weather data from radars and compute data products used for timely response to weather events. The workflows are orchestrated by the Pegasus workflow management system and were chosen because of their diverse resource requirements. We show that our approach results in timely processing of Nowcast workflows under different infrastructure configurations and network conditions. We also show how workflow task clustering choices affect throughput of an ensemble of Nowcast workflows with improved turnaround times. Additionally, we find that using our network-centric platform powered by advanced layer2 networking techniques results in faster, more reliable data throughput, makes cloud resources easier to provision, and the workflows easier to configure for operational use and automation.