Abstract-In the framework of Network Function Virtualization (NFV), we address in this work the design and sizing of Cloud-RAN architectures. We concretely investigate the execution time of software-based Base Band Units (BBUs) on multicore systems. Since Cloud-RAN requires a real-time behavior, we use parallel programming techniques in order to minimize the runtime of BBU functions. For an efficient utilization of computing resources, we investigate the relevance of resource pooling where a global scheduling algorithm allocates processing units to runnable BBU-jobs. We specifically examine the gain that can be obtained when applying data parallelism on the channel decoding BBU-function which is the most expensive one in terms of processing time. Performance results show a significant reduction in the runtime of PHY functions which enables the deployment of a fully centralized Cloud-RAN architecture.

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To meet ever more stringent requirements in terms of latency, 5G/6G networks are evolving from centralized to distributed architectures, for which the cloud-native paradigm with services decomposed into microservices is utmost relevant. This in turn raises the issue related to the distribution of network functions. In this paper, we introduce a novel microservice placement strategy considering the internal service composition, notably the communication between microservices. We formulate the placement as an optimization problem with the aim of minimizing end-to-end service latency. We solve the optimization problem with a combination of greedy and genetic algorithms.