53 citations · 58 across the 4 of their papers we have counts for
7 papers
Finding Adversarial Inputs for Heuristics using Multi-level Optimization
Pooria Namyar, Behnaz Arzani, Ryan Beckett +5
Production systems use heuristics because they are faster or scale better than their optimal counterparts. Yet, practitioners are often unaware of the performance gap between a heu…
FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stations
Jinhyun So, Kevin Hsieh, Behnaz Arzani +3
Large-scale deployments of low Earth orbit (LEO) satellites collect massive amount of Earth imageries and sensor data, which can empower machine learning (ML) to address global cha…
Towards a Cost vs. Quality Sweet Spot for Monitoring Networks
Nofel Yaseen, Behnaz Arzani, Krishna Chintalapudi +6
Continuously monitoring a wide variety of performance and fault metrics has become a crucial part of operating large-scale datacenter networks. In this work, we ask whether we can…
Interpret-able feedback for AutoML systems
Behnaz Arzani, Kevin Hsieh, Haoxian Chen
Automated machine learning (AutoML) systems aim to enable training machine learning (ML) models for non-ML experts. A shortcoming of these systems is that when they fail to produce…
Towards A Domain-Customized Automated Machine Learning Framework For Networks and Systems
Behnaz Arzani, Bita Rouhani
Clouds gather a vast volume of telemetry from their networked systems which contain valuable information that can help solve many of the problems that continue to plague them. Howe…
Sunstar: A Cost-effective Multi-Server Solution for Reliable Video Delivery
Behnaz Arzani, Nicholas Iodice, Steven Hwang +3
In spite of much progress and many advances, cost-effective, high-quality video delivery over the internet remains elusive. To address this ongoing challenge, we propose Sunstar, a…