53 citations · 71 across the 16 of their papers we have counts for
17 papers
Harvest: Adaptive Photonic Switching Schedules for Collective Communication in Scale-up Domains
Mahir Rahman, Samuel Joseph, Nihar Kodkani +2
As chip-to-chip silicon photonics gain traction for their bandwidth and energy efficiency, their circuit-switched nature raises a fundamental question for collective communication:…
Dynamic Rebatching for Efficient Early-Exit Inference with DREX
Xuting Liu, Daniel Alexander, Siva Kesava Reddy Kakarla +2
Early-Exit (EE) is a Large Language Model (LLM) architecture that accelerates inference by allowing easier tokens to be generated using only a subset of the model's layers. However…
A Performance Analyzer for a Public Cloud's ML-Augmented VM Allocator
Roozbeh Bostandoost, Pooria Namyar, Siva Kesava Reddy Kakarla +8
Cloud operators increasingly deploy multiple ML models in their VM allocation pipelines. In such settings, individually benign predictions can shift and compound, severely degradin…
Robust Heuristic Algorithm Design with LLMs
Pantea Karimi, Dany Rouhana, Pooria Namyar +3
We posit that we can generate more robust and performant heuristics if we augment approaches using LLMs for heuristic design with tools that explain why heuristics underperform and…
Towards Safer Heuristics With XPlain
Pantea Karimi, Solal Pirelli, Siva Kesava Reddy Kakarla +5
Many problems that cloud operators solve are computationally expensive, and operators often use heuristic algorithms (that are faster and scale better than optimal) to solve them m…
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…
Solving Max-Min Fair Resource Allocations Quickly on Large Graphs
Pooria Namyar, Behnaz Arzani, Srikanth Kandula +5
We consider the max-min fair resource allocation problem. The best-known solutions use either a sequence of optimizations or waterfilling, which only applies to a narrow set of cas…
Everything Matters in Programmable Packet Scheduling
Albert Gran Alcoz, Balázs Vass, Gábor Rétvári +1
Programmable packet scheduling allows the deployment of scheduling algorithms into existing switches without need for hardware redesign. Scheduling algorithms are programmed by tag…
Rethinking Machine Learning Collective Communication as a Multi-Commodity Flow Problem
Behnaz Arzani, Siva Kesava Reddy Kakarla, Miguel Castro +3
We show communication schedulers' recent work proposed for ML collectives does not scale to the increasing problem sizes that arise from training larger models. These works also of…
Enhancing Network Failure Mitigation with Performance-Aware Ranking
Pooria Namyar, Arvin Ghavidel, Daniel Crankshaw +5
Cloud providers install mitigations to reduce the impact of network failures within their datacenters. Existing network mitigation systems rely on simple local criteria or global p…
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…
007: Democratically Finding The Cause of Packet Drops
Behnaz Arzani, Selim Ciraci, Luiz Chamon +5
Network failures continue to plague datacenter operators as their symptoms may not have direct correlation with where or why they occur. We introduce 007, a lightweight, always-on…
FixRoute: A Unified Logic and Numerical Tool for Provably Safe Internet Traffic Engineering
Behnaz Arzani, Alexander Gurney, Bo Li +3
The performance of networks that use the Internet Protocol is sensitive to precise configuration of many low-level parameters on each network device. These settings govern the acti…