  Network slicing provides introduces customized and agile network deployment
for managing different service types for various verticals under the same
infrastructure. To cater to the dynamic service requirements of these verticals
and meet the required quality-of-service (QoS) mentioned in the service-level
agreement (SLA), network slices need to be isolated through dedicated elements
and resources. Additionally, allocated resources to these slices need to be
continuously monitored and intelligently managed. This enables immediate
detection and correction of any SLA violation to support automated service
assurance in a closed-loop fashion. By reducing human intervention, intelligent
and closed-loop resource management reduces the cost of offering flexible
services. Resource management in a network shared among verticals (potentially
administered by different providers), would be further facilitated through open
and standardized interfaces. Open radio access network (O-RAN) is perhaps the
most promising RAN architecture that inherits all the aforementioned features,
namely intelligence, open and standard interfaces, and closed control loop.
Inspired by this, in this article we provide a closed-loop and intelligent
resource provisioning scheme for O-RAN slicing to prevent SLA violations. In
order to maintain realism, a real-world dataset of a large operator is used to
train a learning solution for optimizing resource utilization in the proposed
closed-loop service automation process. Moreover, the deployment architecture
and the corresponding flow that are cognizant of the O-RAN requirements are
also discussed.
