Resource-aware edge-based stream analytics

Petri, Ioan ORCID: https://orcid.org/0000-0002-1625-8247, Chirila, Ioan, Gomes, Heitor, Bifet, Albert and Rana, Omer ORCID: https://orcid.org/0000-0003-3597-2646 2022. Resource-aware edge-based stream analytics. IEEE Internet Computing 26 (4) , pp. 79-88. 10.1109/MIC.2022.3152478

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Abstract

Understanding how machine learning algorithms can be used for stream processing on edge devices remains an important challenge. Such ML algorithms can be represented as operators and dynamically adapted based on the resources on which they are hosted. Deploying machine learning algorithms on edge resources often focuses on carrying out inference on the edge, whilst learning and model development takes place on a cloud data center. We describe TinyMOA, a modified version of the open-source Massive Online Analytics (MOA) library for stream processing, that can be deployed across both local and remote edge resources using the Parsl and Kafka systems. Using an experimental testbed, we demonstrate how machine learning stream processing operators can be configured based on the resource on which they are hosted, and discuss subsequent implications for edge-based stream processing systems.

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:	22 February 2022
Date of Acceptance:	18 February 2022
Last Modified:	18 Nov 2024 21:30
URI:	https://orca.cardiff.ac.uk/id/eprint/147720

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