Network virtualization, software-defined infrastructure, and orchestration are pivotal elements in contemporary networks, yielding new vectors for optimization and novel capabilities. In line with these principles, O-RAN presents an avenue to bypass vendor lock-in, circumvent vertical configurations, enable network programmability, and facilitate integrated Artificial Intelligence (AI) support. Moreover, modern container orchestration frameworks (e.g., Kubernetes, Red Hat OpenShift) simplify the way cellular base stations, as well as the newly introduced RAN Intelligent Controllers (RICs), are deployed, managed, and orchestrated. While this enables cost reduction via infrastructure sharing, it also makes it more challenging to meet O-RAN control latency requirements, especially during peak resource utilization. To address this problem, we propose ScalO-RAN, a control framework rooted in optimization and designed as an O-RAN rApp that allocates and scales AI-based O-RAN applications (xApps, rApps, dApps) to: (i) abide by application-specific latency requirements, and (ii) monetize the shared infrastructure while reducing energy consumption. We prototype ScalO-RAN on an OpenShift cluster with base stations, RIC, and a set of AI-based xApps deployed as micro-services. We evaluate ScalO-RAN both numerically and experimentally. Our results show that ScalO-RAN can optimally allocate and distribute O-RAN applications within available computing nodes to accommodate even stringent latency requirements. More importantly, we show that scaling O-RAN applications is primarily a time-constrained problem rather than a resource-constrained one, where scaling policies must account for stringent inference time of AI applications, and not only how many resources they consume.

翻译：摘要：网络虚拟化、软件定义基础设施及编排是当代网络中的关键要素，为优化和新功能实现提供了新途径。遵循这些原则，O-RAN为突破供应商锁定、避免垂直配置、实现网络可编程性及集成人工智能（AI）支持开辟了道路。此外，现代容器编排框架（如Kubernetes、Red Hat OpenShift）简化了蜂窝基站及新引入的RAN智能控制器（RIC）的部署、管理与编排方式。虽然这通过基础设施共享降低了成本，但也使得满足O-RAN控制时延要求（尤其在高资源利用率期间）更具挑战性。为解决此问题，我们提出ScalO-RAN——一种基于优化理论设计、作为O-RAN rApp实现的控制框架，用于分配和扩展基于AI的O-RAN应用（xApps、rApps、dApps），以实现：（i）满足应用特定的时延要求，（ii）在降低能耗的同时实现共享基础设施的货币化。我们在包含基站、RIC及一组以微服务形式部署的基于AI的xApp的OpenShift集群上实现了ScalO-RAN原型系统，并从数值仿真和实验两个维度对其进行评估。结果表明，ScalO-RAN能在可用计算节点间最优分配和分布O-RAN应用，即使对严苛时延要求也能满足。更重要的是，我们发现O-RAN应用的扩展本质上是时间约束问题而非资源约束问题——扩展策略必须考虑AI应用的严格推理时间，而不仅仅是其资源消耗量。