# ESG: Pipeline-Conscious Efficient Scheduling of DNN Workflows on Serverless Platforms with Shareable GPUs

arXiv: https://arxiv.org/abs/2404.16812 · HTML: https://mlsanity.com/paper/2404.16812/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2024.

## Built on by (most cited first, 2 of 2)

- [Adaptive GPU Resource Allocation for Multi-Agent Collaborative Reasoning in Serverless Environments](https://mlsanity.com/paper/2512.22149/) (2025)
- [High-Performance Serverless Computing: A Systematic Literature Review on Serverless for HPC, AI, and Big Data](https://mlsanity.com/paper/2601.09334/) (2026)

## Abstract (arXiv)

Recent years have witnessed increasing interest in machine learning inferences on serverless computing for its auto-scaling and cost effective properties. Existing serverless computing, however, lacks effective job scheduling methods to handle the schedule space dramatically expanded by GPU sharing, task batching, and inter-task relations. Prior solutions have dodged the issue by neglecting some important factors, leaving some large performance potential locked. This paper presents ESG, a new scheduling algorithm that directly addresses the difficulties. ESG treats sharable GPU as a first-order factor in scheduling. It employs an optimality-guided adaptive method by combining A*-search and a novel dual-blade pruning to dramatically prune the scheduling space without compromising the quality. It further introduces a novel method, dominator-based SLO distribution, to ensure the scalability of the scheduler. The results show that ESG can significantly improve the SLO hit rates 61%-80% while saving 47%-187% costs over prior work.