  The next generation of cellular networks will be characterized by
softwarized, open, and disaggregated architectures exposing analytics and
control knobs to enable network intelligence. How to realize this vision,
however, is largely an open problem. In this paper, we take a decisive step
forward by presenting and prototyping OrchestRAN, a novel orchestration
framework that embraces and builds upon the Open RAN paradigm to provide a
practical solution to these challenges. OrchestRAN has been designed to execute
in the non-real-time RAN Intelligent Controller (RIC) and allows Network
Operators (NOs) to specify high-level control/inference objectives (i.e., adapt
scheduling, and forecast capacity in near-real-time for a set of base stations
in Downtown New York). OrchestRAN automatically computes the optimal set of
data-driven algorithms and their execution location to achieve intents
specified by the NOs while meeting the desired timing requirements. We show
that the problem of orchestrating intelligence in Open RAN is NP-hard, and
design low-complexity solutions to support real-world applications. We
prototype OrchestRAN and test it at scale on Colosseum. Our experimental
results on a network with 7 base stations and 42 users demonstrate that
OrchestRAN is able to instantiate data-driven services on demand with minimal
control overhead and latency.
