Orchestrating networked machine learning applications using Autosteer

Wen, Zhenyu, Hu, Haozhen, Yang, Renyu, Qian, Bin, Sham, Ringo W. H., Sun, Rui, Xu, Jie, Patel, Pankesh, Rana, Omer ORCID: https://orcid.org/0000-0003-3597-2646, Dustdar, Schahram and Ranjan, Rajiv 2022. Orchestrating networked machine learning applications using Autosteer. IEEE Internet Computing 26 (6) , pp. 51-58. 10.1109/MIC.2022.3180907

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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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	1089-7801
Date of First Compliant Deposit:	24 January 2023
Date of Acceptance:	5 September 2022
Last Modified:	26 Nov 2024 03:15
URI:	https://orca.cardiff.ac.uk/id/eprint/156196

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