MERIF 2026 - MERIF26 Demo and Poster Night

DEMO NIGHT SIGN UP!

Showcase your innovative projects at MERIF26's Demo Night! Share the work you've been developing by filling out the form to be considered.

If you’ve never participated in a Demo/Poster Night, the event is set up like a trade show, where each presenter is stationed at their own booth (a table around the room). Attendees will visit booths to learn about your experiments, ask questions, and engage with your work. At the end of the evening, attendees will vote for their favorite demo, and the winner will be featured in a blog post on our website and LinkedIn, receive a MERIF Best Demo Certificate, and be highlighted in the next newsletter.

Please complete the form by August 31, 2026.

Demo and Poster Night Information

Location

AC Hotel Washington DC Downtown

PH Level

1112 19th Street NW, Washington, District of Columbia, USA, 20036

Time

5:30-7:30 pm ET

Demo night presenters: Please arrive to set up your demo no later than 5:15 pm, the event will begin promptly at 5:50 pm

Guests: please relax in the lobby until 5:30 pm to allow presenters to set up their demos. Thank you!

Demo and Poster Night Participants

Demo 1: AI Cluster Monitoring on the Fabric Testbed: Live Visibility from Kernel to Application

Demo 2: Making Flexible Network Measurements Safe to Share for Research

Demo 3: Adaptive Compression on-the-go for Federated Data Transfer in High-Throughput Scientific Workloads

Demo 4: AdaptiveOn: Proactive Scheduling for Deterministic Round-Trip Networking in 5G

Demo 1: AI Cluster Monitoring on the Fabric Testbed: Live Visibility from Kernel to Application

Presenter: Manas Das

This showcase introduces an extensible AI cluster resource monitoring tool that delivers live, browser-based performance dashboards for Fabric users. This tool can be extended to other platforms/testbeds. It provides comprehensive visibility from the kernel to the application level, complete with support for custom monitoring code integration.

Demo 2: Making Flexible Network Measurements Safe to Share for Research

Presenter: Nik Sultana

This demo showcases a new approach that provides sound, machine-checked guarantees over the analysis of network data. This approach was developed to ease the sharing of network data for research, and it builds on the Patchwork system that runs on FABRIC.

Demo 3: Adaptive Compression on-the-go for Federated Data Transfer in High-Throughput Scientific Workloads

Presenter: Venkat Sai Suman Lamba Karanam

Data-intensive workflows in high-throughput computing (HTC) move large amounts of data across sites connected via a dedicated distributed WAN. Timely delivery of required data (for data locality) not only has direct impacts on the end-end job performance but also on the resource utilization (bandwidth, compute). We developed and deployed the core adaptive compression methodology on Chameleon, FABRIC, and other testbeds for validation with full layer-2 control (happy to share results of this validation). This work is aimed at reducing the data transfer costs in distributed WAN ecosystems; particular domains include High-Energy Physics, large-scale genomics and climate modeling. Our current extension includes Rust and Lean based compression implementations of existing compression algorithms (lz4, bz2, snappy, lzma, zstd & sz, zfp, mgard). We found that the combination of implementation+compression algorithm choice is just as important. For example, we saw combinatorial gains in compress/decompress overheads. We will also discuss our prototypes Julia implementation of these compression algorithms and the consequential trade-offs.

Demo 4: AdaptiveOn: Proactive Scheduling for Deterministic Round-Trip Networking in 5G

Presenter: Qiang Liu and Ming Zhao

In this poster, we propose AdaptiveOn, a new wireless networking framework, to support deterministic round-trip networking for network-initiated traffic in 5G. The key idea is to proactively allocate the uplink grants to UEs with sufficient transport block size (TBS), so that UEs can send turnaround traffic immediately, without going through the conventional uplink request-report-grant pipeline. By designing four integral modules (payload estimator, timing learner, MCS adapter, and temporal averager), AdaptiveOn substantially reduces the queuing, segmentation, and re-transmission delay in both uplink and downlink transmission.

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