Enhancing performance of machine learning tasks on edge-cloud infrastructures: A cross-domain internet of things based framework

Almurshed, Osama, Kaushal, Ashish, Meshoul, Souham, Muftah, Asmail, Almoghamis, Osama, Petri, Ioan ORCID: https://orcid.org/0000-0002-1625-8247, Auluck, Nitin and Rana, Omer ORCID: https://orcid.org/0000-0003-3597-2646 2024. Enhancing performance of machine learning tasks on edge-cloud infrastructures: A cross-domain internet of things based framework. Future Generation Computer Systems: The International Journal of eScience 10.1016/j.future.2024.107696

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Abstract

The Internet of Things (IoT) and Edge-Cloud Computing have been trending technologies over the past few years. In this work, we introduce the Enhanced Optimized-Greedy Nominator Heuristic (EO-GNH), a framework designed to optimize machine learning (ML) and artificial intelligence (AI) application placement in edge environments, aiming to improve Quality of Service (QoS). Developed specifically for sectors such as smart agriculture, industry, and healthcare, EO-GNH integrates asynchronous MapReduce and parallel meta-heuristics to effectively manage AI applications, focusing on execution performance, resource utilization, and infrastructure resilience. The framework carefully addresses the distribution challenges of AI applications, especially Service Function Chains (SFCs), in edge-cloud infrastructures. It contains Data Flow Management, which covers aspects of data storage and data privacy, and also considers factors like regional adaptations, mobile access, and AI model refinement. EO-GNH ensures high availability for forecasting, prediction, and training AI models, operating efficiently within a geo-distributed infrastructure. The proposed strategies within EO-GNH emphasize concurrent multi-node execution, enhancing AI application placement by improving execution time, dependability, and cost-effectiveness. The efficiency of EO-GNH is demonstrated through its impact on QoS in real-time resource management across three application domains, highlighting its adaptability and potential in diverse cross-domain IoT-based environments.

Item Type:	Article
Date Type:	Published Online
Status:	In Press
Schools:	Computer Science & Informatics
Additional Information:	License information from Publisher: LICENSE 1: Title: This article is under embargo with an end date yet to be finalised.
Publisher:	Elsevier
ISSN:	0167-739X
Date of First Compliant Deposit:	6 January 2025
Date of Acceptance:	22 December 2024
Last Modified:	06 Jan 2025 11:15
URI:	https://orca.cardiff.ac.uk/id/eprint/175008

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