  Contemporary Distributed Computing Systems (DCS) such as Cloud Data Centres
are large scale, complex, heterogeneous, and distributed across multiple
networks and geographical boundaries. On the other hand, the Internet of Things
(IoT)-driven applications are producing a huge amount of data that requires
real-time processing and fast response. Managing these resources efficiently to
provide reliable services to end-users or applications is a challenging task.
The existing Resource Management Systems (RMS) rely on either static or
heuristic solutions inadequate for such composite and dynamic systems. The
advent of Artificial Intelligence (AI) due to data availability and processing
capabilities manifested into possibilities of exploring data-driven solutions
in RMS tasks that are adaptive, accurate, and efficient. In this regard, this
paper aims to draw the motivations and necessities for data-driven solutions in
resource management. It identifies the challenges associated with it and
outlines the potential future research directions detailing where and how to
apply the data-driven techniques in the different RMS tasks. Finally, it
provides a conceptual data-driven RMS model for DCS and presents the two
real-time use cases (GPU frequency scaling and data centre resource management
from Google Cloud and Microsoft Azure) demonstrating AI-centric approaches'
feasibility.
