Poster: Flexible Scheduling of Network and Computing Resources for Distributed AI Tasks

2024/07/05 by Wang, Ruikun, Zhang, Jiawei, Zhang, Qiaolun +5 · 1 citation
#FOS: Computer and information sciences #Networking and Internet Architecture (cs.NI)

paper · doi:10.48550/arxiv.2407.04845

Abstract

Many emerging Artificial Intelligence (AI) applications require on-demand provisioning of large-scale computing, which can only be enabled by leveraging distributed computing services interconnected through networking. To address such increasing demand for networking to serve AI tasks, we investigate new scheduling strategies to improve communication efficiency and test them on a programmable testbed. We also show relevant challenges and research directions.

Cited by

• On the Optimization of Model Aggregation for Federated Learning at the Network Edge

Related

• Kant: An Efficient Unified Scheduling System for Large-Scale AI Clusters
• How Can AI be Distributed in the Computing Continuum? Introducing the Neural Pub/Sub Paradigm
• Jupiter: A Networked Computing Architecture
• INSIGHT: A Survey of In-Network Systems for Intelligent, High-Efficiency AI and Topology Optimization
• A Dynamic Distributed Scheduler for Computing on the Edge