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Network virtualization, software-defined infrastructure, and orchestrationare pivotal elements in contemporary networks, yielding new vectors foroptimization and novel capabilities. In line with these principles, O-RANpresents an avenue to bypass vendor lock-in, circumvent verticalconfigurations, enable network programmability, and facilitate integratedArtificial Intelligence (AI) support. Moreover, modern container orchestrationframeworks (e.g., Kubernetes, Red Hat OpenShift) simplify the way cellular basestations, as well as the newly introduced RAN Intelligent Controllers (RICs),are deployed, managed, and orchestrated. While this enables cost reduction viainfrastructure sharing, it also makes it more challenging to meet O-RAN controllatency requirements, especially during peak resource utilization. To addressthis problem, we propose ScalO-RAN, a control framework rooted in optimizationand designed as an O-RAN rApp that allocates and scales AI-based O-RANapplications (xApps, rApps, dApps) to: (i) abide by application-specificlatency requirements, and (ii) monetize the shared infrastructure whilereducing energy consumption. We prototype ScalO-RAN on an OpenShift clusterwith base stations, RIC, and a set of AI-based xApps deployed asmicro-services. We evaluate ScalO-RAN both numerically and experimentally. Ourresults show that ScalO-RAN can optimally allocate and distribute O-RANapplications within available computing nodes to accommodate even stringentlatency requirements. More importantly, we show that scaling O-RAN applicationsis primarily a time-constrained problem rather than a resource-constrained one,where scaling policies must account for stringent inference time of AIapplications, and not only how many resources they consume.

ScalO-RAN: Energy-aware Network Intelligence Scaling in Open RAN