DETERMINISTIC NETWORKING ENABLERS Sample Clauses

DETERMINISTIC NETWORKING ENABLERS. The project targets to develop Programming Protocol-independent Packet Processors (P4) modules to provide deterministic communications over the 6G network as shown in Figure 8. This type of connectivity will be key at the data plane, such as the backhaul (5G core - CU), fronthaul (DU-RU) and User Plane Function (UPF). First, the P4 modules can be part of the trial networks and can be used in the project to demonstrate the KPIs improvement (e.g., latency, reliability, security, and energy consumption). The first planned developments will be the implementation of UPF (Rel. 16) and non-invasive telemetry at interfaces such as N3, N5, or F1. The P4 modules will be exposed to other modules (i.e., through APIs), for example, to improve the performance of the Zero-Touch Management of the 6G network. In addition, the intention is that the P4 modules can continue to be used and improved in open calls. Figure 8. P4 Modules and TSN Over 5G Architecture at University of Malaga Platform Secondly, the AI/ML framework for DetNet will also be designed and demonstrated to enable intelligent deterministic network and service management. From the perspective of end-to-end performance guarantee within the DetNet, the AI/ML capacity should span from the radio access networks to the transport networks to facilitate the deterministic network connection in a large-scale trial network. The AI/ML framework will include several entities, i.e., monitoring system, analysis engine, decision engine, and API for users. The monitoring system will collect the network metrics and user dynamics and feed them to the analysis engine for further analysis. Based on the network statistic from the underlying network through e.g., netconf, the analysis engine will provide advanced insight on how to configure, operate, and manage the trial networks to ensure the end-to-end deterministic performance. After that, the decision engine will determine how to configure, maintain, or control the trial network infrastructure based on some pre-defined objectives by users using e.g., reinforcement learning. An AI/ML-based deterministic network and service close management loop will be supported by this framework and demonstrated on the trial network testbed.
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