AI for Next Generation Computing: Emerging Trends and Future Directions

Dr Sukhpal Singh Gill
2 min readMay 10, 2022

Highlights

•We investigate the prospects of AI/ML-based next generation computing systems.

•Integrating emerging technologies and AI-enhanced computing paradigms.

•Trends & open challenges of AI-based cloud, fog, edge, serverless & quantum computing.

•We discuss next generation computing with embedded intelligence and Explainable AI.

•We identify benefits & potential risks of computing approaches using AI/ML algorithms.

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.

Fig. 1. AI-Integrated Emerging computing paradigms
Fig. 2. Summary of emerging trends and future directions for AI-integrated next generation computing.

Bibliography

Sukhpal Singh Gill, Minxian Xu, Carlo Ottaviani, Panos Patros, Rami Bahsoon, Arash Shaghaghi, Muhammed Golec, Vlado Stankovski, Huaming Wu, Ajith Abraham, Manmeet Singh, Harshit Mehta, Soumya K. Ghosh, Thar Baker, Ajith Kumar Parlikad, Hanan Lutfiyya, Salil S. Kanhere, Rizos Sakellariou, Schahram Dustdar, Omer Rana, Ivona Brandic, Steve Uhlig, AI for next generation computing: Emerging trends and future directions, Internet of Things, 19, 100514, 1–34, 2022. https://doi.org/10.1016/j.iot.2022.100514

--

--

Dr Sukhpal Singh Gill

Dr. Gill is Assistant Professor in Queen Mary University of London. He is Associate Editor in Elsevier IoT, Wiley ETT & IET Networks. W: https://www.ssgill.me.