Wen, Z, Hu, H, Yang, R orcid.org/0000-0001-6334-4925 et al. (8 more authors) (Cover date: 01 Nov.-Dec. 2022) Orchestrating Networked Machine Learning Applications Using Autosteer. IEEE Internet Computing, 26 (6). pp. 51-58. ISSN 1089-7801
Abstract
A platform for orchestrating networked machine learning (ML) applications over distributed environments is described. ML applications are transformed into automated pipelines that manage the whole application lifecycle and production-grade implementations are automatically constructed. We present AUTOSTEER, a software platform that can deploy ML applications on various hardware resources—interconnected using heterogeneous network resources—across cloud and edge devices. Device placement optimization and model adaptation are used as control actions to support application requirements and maximize the performance of ML model execution over heterogeneous computing resources. The performance of deployed applications is continually monitored at runtime to overcome performance degradation due to incorrect application parameter settings or model decay. Three real-world applications are used to demonstrate how AUTOSTEER can support application deployment and runtime performance guarantees.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/T01461X/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 06 Oct 2022 13:52 |
Last Modified: | 01 Apr 2023 01:14 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/MIC.2022.3180907 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:191185 |