AI for next generation computing: Emerging trends and future directions

Gill, Sukhpal Singh, Xu, Minxian, Ottaviani, Carlo, Patros, Panos, Bahsoon, Rami, Shaghaghi, Arash, Golec, Muhammed, Stankovski, Vlado, Wu, Huaming, Abraham, Ajith, Singh, Manmeet, Mehta, Harshit, Ghosh, Soumya K., Baker, Thar, Parlikad, Ajith Kumar, Lutfiyya, Hanan, Kanhere, Salil S., Sakellariou, Rizos, Dustdar, Schahram, Rana, Omer ORCID: https://orcid.org/0000-0003-3597-2646, Brandic, Ivona and Uhlig, Steve 2022. AI for next generation computing: Emerging trends and future directions. Internet of Things 19 , 100514. 10.1016/j.iot.2022.100514

Full text not available from this repository.

Abstract

Autonomic computing investigates how systems can achieve (user) specified “control” outcomes on their own, without the intervention of a human operator. Autonomic computing fundamentals have been substantially influenced by those of control theory for closed and open-loop systems. In practice, complex systems may exhibit a number of concurrent and inter-dependent control loops. Despite research into autonomic models for managing computer resources, ranging from individual resources (e.g., web servers) to a resource ensemble (e.g., multiple resources within a data centre), research into integrating Artificial Intelligence (AI) and Machine Learning (ML) to improve resource autonomy and performance at scale continues to be a fundamental challenge. The integration of AI/ML to achieve such autonomic and self-management of systems can be achieved at different levels of granularity, from full to human-in-the-loop automation. In this article, leading academics, researchers, practitioners, engineers, and scientists in the fields of cloud computing, AI/ML, and quantum computing join to discuss current research and potential future directions for these fields. Further, we discuss challenges and opportunities for leveraging AI and ML in next generation computing for emerging computing paradigms, including cloud, fog, edge, serverless and quantum computing environments.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Elsevier
ISSN:	2542-6605
Date of Acceptance:	24 February 2022
Last Modified:	10 Nov 2022 10:48
URI:	https://orca.cardiff.ac.uk/id/eprint/148229

Citation Data

Cited 39 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item