76 citations · 131 across the 20 of their papers we have counts for
13 papers · 1 filter
Ofan: Optimal Load Balancing for AI Training
Sarah McClure, Evyatar Cohen, Jakob Krebs +5
The extreme collective completion time (CCT) demands of AI workloads challenge existing packet spraying algorithms, which can have trouble efficiently load-balancing workloads that…
On Topology's Role in ML Training Performance
Sarah McClure, Tegan Wilson, Brad Karp +4
Modern machine learning training workloads run on large-scale networks of compute accelerators. The networks commonly deployed in these systems are typically variations of two basi…
EnCoR: An end-to-end architecture for simplifying cellular networks
Wesley Woo, Zhuowei Wen, Monniiesh Velmurugan +4
Since their creation, cellular networks have made in-network mobility support a key feature of their service model. While this approach provides seamless connectivity for legacy tr…
GATE: GPU-Accelerated Traffic Engineering for the WAN
Rahul Bothra, Alexander Krentsel, Saptarshi Mandal +4
Traffic engineering (TE) has become a crucial tool for enforcing routing policy and maintaining operational efficiency in large networks. Existing TE solutions pick an objective fu…
Rethinking Network Topologies for Cost-Effective Mixture-of-Experts LLM Serving
Junsun Choi, Sam Son, Sunjin Choi +5
Mixture-of-experts (MoE) architectures have turned LLM serving into a cluster-scale workload in which communication consumes a considerable portion of LLM serving runtime. This has…
Load Balancing for AI Training Workloads
Sarah McClure, Evyatar Cohen, Alex Shpiner +4
The extreme bandwidth demands of AI training has made load-balancing a critical component in AI fabrics, and a variety of load-balancing designs have emerged in recent work from bo…
Managing Bandwidth: The Key to Cloud-Assisted Autonomous Driving
Alexander Krentsel, Peter Schafhalter, Joseph E. Gonzalez +3
Prevailing wisdom asserts that one cannot rely on the cloud for critical real-time control systems like self-driving cars. We argue that we can, and must. Following the trends of i…
DBNet: Leveraging DBMS for Network Automation
Rithvik Chuppala, Silvery Fu, Sylvia Ratnasamy
We present DBNet, a data-driven network automation framework built on top of a DBMS. DBNet utilizes key primitives of a DBMS including tables, procedures, transactions, logging, an…
Human-Scale Computing: A Case for Progressive Narrow Waist for Internet Applications
Silvery Fu, Pratyush Das, Sylvia Ratnasamy
In the era where personal devices and applications are pervasive, individuals are continuously generating and interacting with a vast amount of data. Despite this, access to and co…
Revisiting Network Support for RDMA
Radhika Mittal, Alexander Shpiner, Aurojit Panda +4
The advent of RoCE (RDMA over Converged Ethernet) has led to a significant increase in the use of RDMA in datacenter networks. To achieve good performance, RoCE requires a lossless…
Universal Packet Scheduling
Radhika Mittal, Rachit Agarwal, Sylvia Ratnasamy +1
In this paper we address a seemingly simple question: Is there a universal packet scheduling algorithm? More precisely, we analyze (both theoretically and empirically) whether ther…
Low latency via redundancy
Ashish Vulimiri, P. Brighten Godfrey, Radhika Mittal +3
Low latency is critical for interactive networked applications. But while we know how to scale systems to increase capacity, reducing latency --- especially the tail of the latency…
Scalable Routing on Flat Names
Ankit Singla, P. Brighten Godfrey, Kevin Fall +2
We introduce a protocol which routes on flat, location-independent identifiers with guaranteed scalability and low stretch. Our design builds on theoretical advances in the area of…