OREO: O-RAN intElligence Orchestration of xApp-based network services

TL;DR

OREO is a novel orchestrator for O-RAN xApps that maximizes network service deployment efficiency by sharing semantically equivalent xApps, reducing resource use, and outperforming existing methods.

Contribution

Introduces OREO, an xApp orchestrator leveraging a multi-layer graph model to optimize sharing, resource allocation, and service deployment in O-RAN networks.

Findings

OREO closely matches the optimal solution for xApp orchestration.

It deploys up to 35% more services than existing methods.

OREO reduces xApp count and resource consumption by around 30%.

Abstract

The Open Radio Access Network (O-RAN) architecture aims to support a plethora of network services, such as beam management and network slicing, through the use of third-party applications called xApps. To efficiently provide network services at the radio interface, it is thus essential that the deployment of the xApps is carefully orchestrated. In this paper, we introduce OREO, an O-RAN xApp orchestrator, designed to maximize the offered services. OREO's key idea is that services can share xApps whenever they correspond to semantically equivalent functions, and the xApp output is of sufficient quality to fulfill the service requirements. By leveraging a multi-layer graph model that captures all the system components, from services to xApps, OREO implements an algorithmic solution that selects the best service configuration, maximizes the number of shared xApps, and efficiently and dynamically allocates resources to them. Numerical results as well as experimental tests performed using our proof-of-concept implementation, demonstrate that OREO closely matches the optimum, obtained by solving an NP-hard problem. Further, it outperforms the state of the art, deploying up to 35% more services with an average of 30% fewer xApps and a similar reduction in the resource consumption.