# [UOM - Free5 gc]
## 6 B5 gemini Use Cases
### Digital Maps for Energy Optimization of Network AI Components' Use Case  
To demonstrate the advantages provided by the architecture proposed in Section 4.3, we present a use case for energy optimization that builds on the DMap concept to optimize the energy consumption of an NDT system by managing its resources. The need for energy consumption has been increasing in communication networks, as they have become more sophisticated and widespread over the years. To cope with the high energy demand, network providers must be able to manage their energy resources more efficiently. ML has proven to be a valuable tool to achieve this goal [77]. The same can be applied to an NDT scenario by using ML methods to optimize the energy usage of the grid by predicting when and where energy usage is highest. In this way, the network can be reconfigured to reduce energy consumption in those specific areas. In this regard, considering that AI components play an increasingly important role in current networks assisting other components in complex decision processes, it can be interesting to use an NDT to optimize their training and inference processes to optimize the energy consumption of the network. In this context, these optimizations can be applied first in the NDT and then seamlessly reproduced on the physical system after validation. With this innovative approach, optimizations can be performed in a more controlled and safer way. In particular, this use case will illustrate how a physical network can be easily attached to a DMap that will support the optimization of the power consumption of the AI components of the network in a controlled manner. In addition, this use case will consider the implementation of a complementary DMap for the generation of synthetic data that can be used during training processes to boost the performance of ML components in the NDT or for exporting such synthetic data to be used by third parties without creating any privacy breach. We schematically represent the described use case in Figure 7.  
We will develop EnOp, a DMap responsible for the energy optimization of the AI components integrated in the real network. The EnOp DMap will be obtained incorporating an Energy Optimization Component (EOC) in the minimal NDT architecture. The EOC component will allow one to optimize ML/DL models in order to reduce their power consumption in their training procedure and in downstream inference tasks over the network. The EOC component will be composed of an energy estimation model and an upgradeable collection of algorithms for optimizing ML/DL models. The energy estimation model will be in charge of defining the energy consumption of the ML/DLmodels, which can be conducted either by measurements on real hardware or using simulated environments [78]. Based on this power state, several algorithms can be applied to optimize the ML/DL components to adjust the power consumption of the network with minimal impact on its performance. In particular, we consider introducing the energy dimension in the automatic search of efficient DNN architectures using NAS. In addition, other techniques such as Quantization, Pruning, Low-rank Factorization, or Knowledge Distillations techniques can be also applied to compact the ML/DL models in order to decrease their memory footprint and accelerate training and inference task. In addition, this use case proposes Gen, a complementary DMap for the generation of synthetic telemetry data (e.g., VM logs, network traffic connections, etc.). The Gen DMap will allow one to generate synthetic data that statistically replicate data of the real system with high fidelity. The synthetic data in combination with the real data can be highly beneficial for the training of ML/DL models because it can serve as a data augmentation technique to improve the performance of these models. Furthermore, the Gen DMap can provide a safe way to export data to third parties involved in cross developments without violating the privacy of data or incurring data breaches.