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The emergence of diverse network applications demands more flexible and responsive resource allocation for networks. Network slicing is a key enabling technology that provides each network service with a tailored set of network resources to satisfy specific service requirements. The focus of this paper is the network slicing of access networks realized by Passive Optical Networks (PONs). This paper proposes a learning-based Dynamic Bandwidth Allocation (DBA) algorithm for PON access networks, considering slice-awareness, demand-responsiveness, and allocation fairness. Our online convex optimization-based algorithm learns the implicit traffic trend over time and determines the most robust window allocation that reduces the average latency. Our simulation results indicate that the proposed algorithm reduces the average latency by prioritizing delay-sensitive and heavily-loaded ONUs while guaranteeing a minimal window allocation to all ONUs.
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Information Bottleneck-Based Domain Adaptation for Hybrid Deep Learning in Scalable Network Slicing
Network slicing enables operators to efficiently support diverse applications on a shared infrastructure. However, the evolving complexity of networks, compounded by inter-cell interference, necessitates agile and adaptable resource management. While deep learning offers solutions for coping with complexity, its adaptability to dynamic configurations remains limited. In this paper, we propose a novel hybrid deep learning algorithm called IDLA (integrated deep learning with the Lagrangian method). This integrated approach aims to enhance the scalability, flexibility, and robustness of slicing resource allocation solutions by harnessing the high approximation capability of deep learning and the strong generalization of classical non-linear optimization methods. Then, we introduce a variational information bottleneck (VIB)-assisted domain adaptation (DA) approach to enhance integrated deep learning and Lagrangian method (IDLA)’s adaptability across diverse network environments and conditions. We propose pre-training a variational information bottleneck (VIB)-based Quality of Service (QoS) estimator, using slice-specific inputs shared across all source domain slices. Each target domain slice can deploy this estimator to predict its QoS and optimize slice resource allocation using the IDLA algorithm. This VIB-based estimator is continuously fine-tuned with a mixture of samples from both the source and target domains until convergence. Evaluating on a multi-cell network with time-varying slice configurations, the VIB-enhanced IDLA algorithm outperforms baselines such as heuristic and deep reinforcement learning-based solutions, achieving twice the convergence speed and 16.52% higher asymptotic performance after slicing configuration changes. Transferability assessment demonstrates a 25.66% improvement in estimation accuracy with VIB, especially in scenarios with significant domain gaps, highlighting its robustness and effectiveness across diverse domains.
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- Award ID(s):
- 2333164
- PAR ID:
- 10632687
- Publisher / Repository:
- IEEE Transactions on Machine Learning in Communications and Networking
- Date Published:
- Journal Name:
- IEEE Transactions on Machine Learning in Communications and Networking
- Volume:
- 2
- ISSN:
- 2831-316X
- Page Range / eLocation ID:
- 1642 to 1660
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TMLCN.2024.3485520
