Enhancing Cloud-Native Resource Allocation with Probabilistic Forecasting Techniques in O-RAN
<|reference_start|>Enhancing Cloud-Native Resource Allocation with Probabilistic Forecasting Techniques in O-RAN: The need for intelligent and efficient resource provisioning for the productive management of resources in real-world scenarios is growing with the evolution of telecommunications towards the 6G era. Technologies such as Open Radio Access Network (O-RAN) can help to build interoperable solutions for the management of complex systems. Probabilistic forecasting, in contrast to deterministic single-point estimators, can offer a different approach to resource allocation by quantifying the uncertainty of the generated predictions. This paper examines the cloud-native aspects of O-RAN together with the radio App (rApp) deployment options. The integration of probabilistic forecasting techniques as a rApp in O-RAN is also emphasized, along with case studies of real-world applications. Through a comparative analysis of forecasting models using the error metric, we show the advantages of Deep Autoregressive Recurrent network (DeepAR) over other deterministic probabilistic estimators. Furthermore, the simplicity of Simple-Feed-Forward (SFF) leads to a fast runtime but does not capture the temporal dependencies of the input data. Finally, we present some aspects related to the practical applicability of cloud-native O-RAN with probabilistic forecasting.<|reference_end|>
@article{kasuluru2024enhancing,
    title={Enhancing Cloud-Native Resource Allocation with Probabilistic
  Forecasting Techniques in O-RAN},
    author={Vaishnavi Kasuluru, Luis Blanco, Engin Zeydan, Albert Bel, Angelos
  Antonopoulos},
    journal={arXiv preprint arXiv:2407.14377},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.14377},
    primaryClass={cs.NI cs.AI cs.DC cs.IT cs.LG math.IT}
}