NAT Lab





ABOUT US

Welcome to NAT (Networking at Tufts) Lab!

We are an interdisciplinary team of researchers with interests spanning performance and reliability of networked systems, accessible technology design, and, more recently, design and deployment of human-AI applications at scale. We are based in the Computer Science department at Tufts University, which is conveniently located about 5 miles from downtown Boston and easily accessible by train.

RESEARCH TEAM

Principal Investigator



Fahad R. Dogar


Ph.D. Students



Abdullah Bin Faisal
Noah Martin
Hiba Eltigani
Rukhshan Haroon
Chami Lamelas


Alumni


NEWS

RESEARCH

Designing for Autistic Contexts



TwIPS: A Large Language Model Powered Texting Application to Simplify Conversational Nuances for Autistic Users

Autistic individuals often experience difficulties in conveying and interpreting emotional tone and non-literal nuances. Many also mask their communication style to avoid being misconstrued by others, spending considerable time and mental effort in the process. To address these challenges in text-based communication, we present TwIPS, which can assist users with: a) deciphering tone and meaning of incoming messages, b) ensuring the emotional tone of their message is in line with their intent and c) coming up with alternate phrasing for messages that could be misconstrued and received negatively by others.

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CustomAR: A Customizable Picture-Based AR Application For Autism Therapists and Educational Professionals

CustomAR is a customizable Augmented Reality (AR) application, designed in collaboration with therapists, that allows them to create and customize picture-based AR experiences for use in an autistic context. Using a 2-week diary study, we gauge whether the application’s customization options and features are sufficient to allow therapists and educational professionals to create AR experiences for the various learning activities they conduct with autistic children, and what challenges they face in this regard.

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Systems for Machine Learning



Towards Providing Reliable Job Completion Time Predictions with PCS

We build a case for providing job completion time predictions to cloud users, similar to the delivery date of a package or arrival time of a booked ride. Our analysis reveals that providing predictability can come at the expense of performance and fairness. Existing cloud scheduling systems optimize for extreme points in the trade-off space, making them either extremely unpredictable or impractical. To address this challenge, we present PCS, a new scheduling framework that aims to provide predictability while balancing other traditional objectives.

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Workload Adaptive Flow Scheduling

Existing flow scheduling schemes for data center networks optimize for a specific workload and performance metric. We present 2D, a new scheduling policy that offers robustness across performance metrics and changing workloads - a ground existing scheduling policies are unable to cover. 2D combines basic scheduling building blocks of multiplexing and serialization in a principled way, ensuring tail optimal performance across workloads while also improving the average (and lower percentiles) completion times.

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Networks and Measurement



Divided at the Edge - Measuring Performance and the Digital Divide of Cloud Edge Data Centers

We measure latency to the current and predicted cloud edge of three major cloud providers around the world. Our measurements use the RIPE Atlas platform targeting cloud regions, AWS Local Zones, and network optimization services that minimize the path to the cloud edge. An analysis of the digital divide shows rising inequality as the relative difference between users closest and farthest from cloud compute increases. We also find this inequality unfairly affects lower income census tracts in the US. This result is extended globally using remotely sensed night time lights as a proxy for wealth. Finally, we demonstrate that low earth orbit satellite internet can help to close this digital divide.

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Measuring and Improving the Reliability of Wide-Area Cloud Paths

Many popular cloud applications use inter-data center paths; yet, little is known about the characteristics of these β€œcloud paths”. Over an eighteen month period, we measure the inter-continental cloud paths of three providers (Amazon, Google, and Microsoft) using client side (VM-to-VM) measurements. We find that cloud paths are more predictable compared to public Internet paths, with an order of magnitude lower loss rate and jitter at the tail compared to public Internet paths.

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FUNDING