20 citations · 29 across the 7 of their papers we have counts for
7 papers
ML-for-ML
Yutong Zhao, Noga H. Rotman, Gianni Antichi +1
AI training workloads are growing rapidly, making their time, energy, and infrastructure costs increasingly important. In shared cloud clusters, training and fine-tuning jobs compe…
Avoiding Cross-Datacenter Collective Congestion via Disaggregated Buffering
Mariano Scazzariello, Noga H. Rotman, Dima Gavrilenko +6
LLM training at the scale of tens of thousands of GPUs now spans multiple datacenters (DC), making cross-DC collectives over long-haul links unavoidable. A critical and overlooked…
RFSeek and Ye Shall Find
Noga H. Rotman, Tiago Ferreira, Hila Peleg +2
Requests for Comments (RFCs) are extensive specification documents for network protocols, but their prose-based format and their considerable length often impede precise operationa…
CloudCast: Characterizing Public Clouds Connectivity
Noga H. Rotman, Yaniv Ben-Itzhak, Aran Bergman +4
Public clouds are one of the most thriving technologies of the past decade. Major applications over public clouds require world-wide distribution and large amounts of data exchange…
Online Safety Assurance for Deep Reinforcement Learning
Noga H. Rotman, Michael Schapira, Aviv Tamar
Recently, deep learning has been successfully applied to a variety of networking problems. A fundamental challenge is that when the operational environment for a learning-augmented…
Pied Piper: Rethinking Internet Data Delivery
Aran Bergman, Israel Cidon, Isaac Keslassy +4
We contend that, analogously to the transition from resource-limited on-prem computing to resource-abundant cloud computing, Internet data delivery should also be adapted to a real…
Internet Congestion Control via Deep Reinforcement Learning
Nathan Jay, Noga H. Rotman, P. Brighten Godfrey +2
We present and investigate a novel and timely application domain for deep reinforcement learning (RL): Internet congestion control. Congestion control is the core networking task o…