Service-aware real-time slicing for virtualized beyond 5G networks

• Published in: Computer networks (Amsterdam, Netherlands : 1999) Netherlands : 1999), 2024-06, Vol.247, p.110445, Article 110445
• Main Authors: Tsourdinis, Theodoros, Chatzistefanidis, Ilias, Makris, Nikos, Korakis, Thanasis, Nikaein, Navid, Fdida, Serge
• Format: Article
• Language: English
• Subjects: Beyond 5G; Kubernetes; Machine Learning; MLOps; OpenAirInterface; RAN slicing; Service-aware
• ISSN: 1389-1286; 1872-7069
• DOI: 10.1016/j.comnet.2024.110445

Edge Intelligence is expected to play a vital role in the evolution of 5G networks, empowering them with the capability to make real-time decisions regarding various allocations related to their management and service provisioning to end-users. This shift facilitates the transition from a network-aware approach, where applications are developed to manage network quality fluctuations, to a service-aware network that self-adjusts based on the hosted applications. In this paper, we design and implement a service-aware network managed from the network edge. We utilize and assess various Machine Learning models to classify cellular network traffic flows in the backhaul, aiming to predict their future impact on network load. Leveraging these predictions, the network can proactively and autonomously reallocate slices in the Radio Access Network via programmable APIs, ensuring the demands of the traffic-generating applications are met. The approach integrates innovative MLOps methodologies for distributed and online training, enabling continuous model refinement and adaptation to evolving network dynamics. Our framework was tested in a real-world environment with realistic traffic scenarios, and the results were evaluated in real-time, down to a granularity of 10ms. Our findings indicate that the network can swiftly adjust to traffic, providing users with slices tailored to their application needs. Notably, our experiments show that under the studied settings, the users experienced up to 4 times lower latency (jitter) and nearly 4 times higher throughput when interacting with various applications, compared to the standard non-AI/ML unit. Furthermore, our dynamic scheme significantly optimizes resource allocation, ensuring energy efficiency by avoiding over- and under-provisioning of resources. [Display omitted] •Developed a custom NWDAF to parse statistics from core and RAN.•Introduced MLOps architecture for cloud-based, distributed training across nodes.•Collected Jitter, Throughput, and CQI metrics from realistic mobility scenarios.•Tested the framework’s performance using six distinct deep-learning models.•Our scheme outperforms a non-AI baseline in efficiency and resource allocation