Predictive Closed-Loop Service Automation in O-RAN based Network Slicing

TL;DR

This paper proposes an intelligent, closed-loop resource management scheme for O-RAN network slicing, utilizing real-world data to optimize resource utilization and prevent SLA violations, thereby enabling automated service assurance.

Contribution

It introduces a novel closed-loop, AI-driven resource provisioning framework specifically designed for O-RAN slicing, validated with real operator data.

Findings

Effective SLA violation prevention through AI-based resource management

Reduced human intervention in network slice management

Improved resource utilization efficiency

Abstract

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.