Presentation Information
[B-6-70]A Proposal for an NRF Load Distribution Method Based on Traffic Prediction
〇Shunsuke Dojiri1 (1. NTT DOCOMO, INC.)
Keywords:
mobile network
In the 6G era, in addition to the explosive growth in the number of devices and sensors, the widespread adoption of AI agents and autonomous mobile robots is expected to give rise to irregular, high-frequency communication traffic that is not dependent on human cycles. Such traffic is a major factor leading to excessive expansion of network infrastructure.
The NRF (Network Repository Function) in the 5G core is responsible for service discovery and state management of all NFs (Network Functions), and as such, is structurally prone to load concentration. To address this challenge, load-balancing methods—such as NRF hierarchical structuring, query control via SCP (Service Communication Proxy), and cache utilization—have already been defined as standard specifications.
In studies toward 6G, use cases are being considered in which not only a single UAV (Uncrewed Aerial Vehicle) but also multiple UAVs collaborate to execute tasks. It is anticipated that the NRF will experience a load during the switching process at switches when a large number of UAVs are in motion.
In this paper, with the aim of avoiding equipment bloat, we propose a new approach—in addition to existing methods—that proactively distributes NRF load based on traffic forecasts.
The NRF (Network Repository Function) in the 5G core is responsible for service discovery and state management of all NFs (Network Functions), and as such, is structurally prone to load concentration. To address this challenge, load-balancing methods—such as NRF hierarchical structuring, query control via SCP (Service Communication Proxy), and cache utilization—have already been defined as standard specifications.
In studies toward 6G, use cases are being considered in which not only a single UAV (Uncrewed Aerial Vehicle) but also multiple UAVs collaborate to execute tasks. It is anticipated that the NRF will experience a load during the switching process at switches when a large number of UAVs are in motion.
In this paper, with the aim of avoiding equipment bloat, we propose a new approach—in addition to existing methods—that proactively distributes NRF load based on traffic forecasts.