6 citations · 8 across the 6 of their papers we have counts for
6 papers
MonkeyTree: Near-Minimal Congestion for Multi-tenant Training via Migration
Anton A. Zabreyko, Weiyang Wang, Manya Ghobadi
We present MonkeyTree, the first system to mitigate network congestion in multi-tenant GPU clusters through job-migration based defragmentation rather than network-layer techniques…
Prediction-Guided Control in Data Center Networks
Kevin Zhao, Chenning Li, Anton A. Zabreyko +7
In this paper, we design, implement, and evaluate Polyphony, a system to give network operators a new way to control and reduce the frequency of poor tail latency events in multi-c…
KramaBench: A Benchmark for AI Systems on Data-to-Insight Pipelines over Data Lakes
Eugenie Lai, Gerardo Vitagliano, Ziyu Zhang +16
Discovering insights from a real-world data lake potentially containing unclean, semi-structured, and unstructured data requires a variety of data processing tasks, ranging from ex…
m4: A Learned Flow-level Network Simulator
Chenning Li, Anton A. Zabreyko, Arash Nasr-Esfahany +4
Flow-level simulation is widely used to model large-scale data center networks due to its scalability. Unlike packet-level simulators that model individual packets, flow-level simu…
MLCC: A Congestion Control Technique to Accelerate ML Training
Anton A. Zabreyko, Sanjoli Narang, Sudarsanan Rajasekaran +1
We present MLCC, a novel technique to augment today's congestion control algorithms to accelerate DNN training jobs in shared GPU clusters in a fully distributed manner. At the hea…
Kernel-as-a-Service: A Serverless Interface to GPUs
Nathan Pemberton, Anton Zabreyko, Zhoujie Ding +2
Serverless computing has made it easier than ever to deploy applications over scalable cloud resources, all the while driving higher utilization for cloud providers. While this tec…