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

TL;DR

This paper explores flexible scheduling strategies for distributed AI tasks, aiming to optimize network and computing resource utilization, tested on a programmable testbed, and discusses future challenges and research directions.

Contribution

It introduces new scheduling strategies for distributed AI workloads that enhance communication efficiency and demonstrates their effectiveness on a programmable testbed.

Findings

Improved communication efficiency in distributed AI tasks

Effective scheduling strategies tested on a programmable testbed

Identified key challenges and future research directions

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.