  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 center), 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.

Distributed, Parallel, and Cluster Computing