Enhancing Cloud-Native Resource Allocation with Probabilistic Forecasting Techniques in O-RAN
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866907985000792064 |
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| author | Kasuluru, Vaishnavi Blanco, Luis Zeydan, Engin Bel, Albert Antonopoulos, Angelos |
| author_facet | Kasuluru, Vaishnavi Blanco, Luis Zeydan, Engin Bel, Albert Antonopoulos, Angelos |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_14377 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Cloud-Native Resource Allocation with Probabilistic Forecasting Techniques in O-RAN Kasuluru, Vaishnavi Blanco, Luis Zeydan, Engin Bel, Albert Antonopoulos, Angelos Networking and Internet Architecture Artificial Intelligence Distributed, Parallel, and Cluster Computing Information Theory Machine Learning 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. |
| title | Enhancing Cloud-Native Resource Allocation with Probabilistic Forecasting Techniques in O-RAN |
| topic | Networking and Internet Architecture Artificial Intelligence Distributed, Parallel, and Cluster Computing Information Theory Machine Learning |
| url | https://arxiv.org/abs/2407.14377 |