ESG: Pipeline-Conscious Efficient Scheduling of DNN Workflows on Serverless Platforms with Shareable GPUs
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866911853396885504 |
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| author | Hui, Xinning Xu, Yuanchao Guo, Zhishan Shen, Xipeng |
| author_facet | Hui, Xinning Xu, Yuanchao Guo, Zhishan Shen, Xipeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_16812 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ESG: Pipeline-Conscious Efficient Scheduling of DNN Workflows on Serverless Platforms with Shareable GPUs Hui, Xinning Xu, Yuanchao Guo, Zhishan Shen, Xipeng Distributed, Parallel, and Cluster Computing 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. |
| title | ESG: Pipeline-Conscious Efficient Scheduling of DNN Workflows on Serverless Platforms with Shareable GPUs |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2404.16812 |