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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.14827 |
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| _version_ | 1866917759782223872 |
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| author | Gudepu, Venkateswarlu Chirumamilla, Bhargav Chintapalli, Venkatarami Reddy Castoldi, Piero Valcarenghi, Luca Tamma, Bheemarjuna Reddy Kondepu, Koteswararao |
| author_facet | Gudepu, Venkateswarlu Chirumamilla, Bhargav Chintapalli, Venkatarami Reddy Castoldi, Piero Valcarenghi, Luca Tamma, Bheemarjuna Reddy Kondepu, Koteswararao |
| contents | Beyond fifth-generation (B5G) networks aim to support high data rates, low-latency applications, and massive machine communications. Artificial Intelligence/Machine Learning (AI/ML) can help to improve B5G network performance and efficiency. However, dynamic service demands of B5G use cases cause AI/ML model performance degradation, resulting in Service Level Agreements (SLA) violations, over- or under-provisioning of resources, etc. Retraining is essential to address the performance degradation of the AI/ML models. Existing threshold and periodic retraining approaches have potential disadvantages, such as SLA violations and inefficient resource utilization for setting a threshold parameter in a dynamic environment. This paper proposes a novel approach that predicts when to retrain AI/ML models using Generative Artificial Intelligence. The proposed predictive approach is evaluated for a Quality of Service Prediction use case on the Open Radio Access Network (O-RAN) Software Community platform and compared to the predictive approach based on the classifier and a threshold approach. Also, a realtime dataset from the Colosseum testbed is considered to evaluate Network Slicing (NS) use case with the proposed predictive approach. The results show that the proposed predictive approach outperforms both the classifier-based predictive and threshold approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_14827 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generative-AI for AI/ML Model Adaptive Retraining in Beyond 5G Networks Gudepu, Venkateswarlu Chirumamilla, Bhargav Chintapalli, Venkatarami Reddy Castoldi, Piero Valcarenghi, Luca Tamma, Bheemarjuna Reddy Kondepu, Koteswararao Networking and Internet Architecture Signal Processing Beyond fifth-generation (B5G) networks aim to support high data rates, low-latency applications, and massive machine communications. Artificial Intelligence/Machine Learning (AI/ML) can help to improve B5G network performance and efficiency. However, dynamic service demands of B5G use cases cause AI/ML model performance degradation, resulting in Service Level Agreements (SLA) violations, over- or under-provisioning of resources, etc. Retraining is essential to address the performance degradation of the AI/ML models. Existing threshold and periodic retraining approaches have potential disadvantages, such as SLA violations and inefficient resource utilization for setting a threshold parameter in a dynamic environment. This paper proposes a novel approach that predicts when to retrain AI/ML models using Generative Artificial Intelligence. The proposed predictive approach is evaluated for a Quality of Service Prediction use case on the Open Radio Access Network (O-RAN) Software Community platform and compared to the predictive approach based on the classifier and a threshold approach. Also, a realtime dataset from the Colosseum testbed is considered to evaluate Network Slicing (NS) use case with the proposed predictive approach. The results show that the proposed predictive approach outperforms both the classifier-based predictive and threshold approaches. |
| title | Generative-AI for AI/ML Model Adaptive Retraining in Beyond 5G Networks |
| topic | Networking and Internet Architecture Signal Processing |
| url | https://arxiv.org/abs/2408.14827 |