28 citations · 92 across the 13 of their papers we have counts for
14 papers
Roomie: Interference-Aware Colocation for Efficient Model Serving
Youssouph Faye, Francescomaria Faticanti, Shubham Jain +1
As demand for DNN inference grows, GPU capacity is increasingly oversubscribed, forcing operators to colocate multiple models on the same device in both cloud and edge deployments.…
Characterizing the Impact of Active Queue Management on Speed Test Measurements
Siddhant Ray, Taveesh Sharma, Jonatas Marques +3
Present day speed test tools measure peak throughput, but often fail to capture the user-perceived responsiveness of a network connection under load. Recently, platforms such as ND…
Cruise Control: Dynamic Model Selection for ML-Based Network Traffic Analysis
Johann Hugon, Paul Schmitt, Anthony Busson +1
Modern networks increasingly rely on machine learning models for real-time insights, including traffic classification, application quality of experience inference, and intrusion de…
Ironing the Graphs: Toward a Correct Geometric Analysis of Large-Scale Graphs
Saloua Naama, Kavé Salamatian, Francesco Bronzino
Graph embedding approaches attempt to project graphs into geometric entities, i.e, manifolds. The idea is that the geometric properties of the projected manifolds are helpful in th…
Optimal Flow Admission Control in Edge Computing via Safe Reinforcement Learning
A. Fox, F. De Pellegrini, F. Faticanti +2
With the uptake of intelligent data-driven applications, edge computing infrastructures necessitate a new generation of admission control algorithms to maximize system performance…
CATO: End-to-End Optimization of ML-Based Traffic Analysis Pipelines
Gerry Wan, Shinan Liu, Francesco Bronzino +2
Machine learning has shown tremendous potential for improving the capabilities of network traffic analysis applications, often outperforming simpler rule-based heuristics. However,…
Internet Localization of Multi-Party Relay Users: Inherent Friction Between Internet Services and User Privacy
Sean Flynn, Francesco Bronzino, Paul Schmitt
Internet privacy is increasingly important on the modern Internet. Users are looking to control the trail of data that they leave behind on the systems that they interact with. Mul…
AC-DC: Adaptive Ensemble Classification for Network Traffic Identification
Xi Jiang, Shinan Liu, Saloua Naama +3
Accurate and efficient network traffic classification is important for many network management tasks, from traffic prioritization to anomaly detection. Although classifiers using p…
LEAF: Navigating Concept Drift in Cellular Networks
Shinan Liu, Francesco Bronzino, Paul Schmitt +5
Operational networks commonly rely on machine learning models for many tasks, including detecting anomalies, inferring application performance, and forecasting demand. Yet, model a…
Characterizing Service Provider Response to the COVID-19 Pandemic in the United States
Shinan Liu, Paul Schmitt, Francesco Bronzino +1
The COVID-19 pandemic has resulted in dramatic changes to the daily habits of billions of people. Users increasingly have to rely on home broadband Internet access for work, educat…
On the Deployability of Augmented Reality Using Embedded Edge Devices
Ayoub Ben-Ameur, Andrea Araldo, Francesco Bronzino
Edge Computing exploits computational capabilities deployed at the very edge of the network to support applications with low latency requirements. Such capabilities can reside in s…
Traffic Refinery: Cost-Aware Data Representation for Machine Learning on Network Traffic
Francesco Bronzino, Paul Schmitt, Sara Ayoubi +3
Network management often relies on machine learning to make predictions about performance and security from network traffic. Often, the representation of the traffic is as importan…
Inferring Streaming Video Quality from Encrypted Traffic: Practical Models and Deployment Experience
Paul Schmitt, Francesco Bronzino, Sara Ayoubi +3
Inferring the quality of streaming video applications is important for Internet service providers, but the fact that most video streams are encrypted makes it difficult to do so. W…
Exploiting Network Awareness to Enhance DASH Over Wireless
Francesco Bronzino, Dragoslav Stojadinovic, Cedric Westphal +1
The introduction of Dynamic Adaptive Streaming over HTTP (DASH) helped reduce the consumption of resource in video delivery, but its client-based rate adaptation is unable to optim…