## 3 Proposed Optimal Orchestration Process
### Implementation Environment  
The experimental validation of our proposed framework employed a comprehensive dual-platform approach, combining the machine learning capabilities of Google Colab with the network simulation features of MATLAB/Simulink R2023b. This integration enabled thorough testing of both the algorithmic components and network behaviours under realistic conditions.  
The Google Colab environment was configured with TensorFlow 2.9.0 and Scikit-learn 1.0.2, running on a 16 GB RAM GPU instance to ensure adequate computational resources for model training and evaluation. LazyPredict 0.2.12 and Ray Tune 2.3.0 were implemented for automated algorithm selection and hyperparameter optimisation, respectively. This configuration provided the necessary computational power for training and evaluating multiple machine learning models simultaneously.  
Network simulations were conducted using MATLAB/Simulink's 5G Toolbox and Communications Toolbox, with a simulation granularity of 1 ms over a 24 h period. The simulation environment was configured to support multiple network slice types, including eMBB slices with throughput ranges of 100 Mbps to 20 Gbps, URLLC slices maintaining latencies below 1 ms with throughput of 1-10 Mbps, and mMTC slices supporting device densities up to 1 million devices per square kilometre.  
The experiments were conducted using the Google Colab integrated development environment alongside MATLAB and Simulink. Google Colab was chosen for its accessibility, scalability, and ability to leverage GPUs and TPUs, which are critical for processing complex machine learning models. The environment was configured as follows:  
* Processor: Tesla K80 GPU (12 GB GDDR5 VRAM) or TPU;
* RAM: 16 GB;
* Python Version: 3.8.x;  
Figure 5: Conceptual network slice showing ML-based service orchestration, mobile services, and consumers.  
* Libraries: LazyPredict, AutoRL, Scikit-learn, TensorFlow, Keras, and Matplotlib.  
These specifications supported the computational demands of training reinforcement learning (RL) models and running simulations under dynamic 5G conditions. Training each model required an average of 3 min per epoch, with total runtimes of approximately 6 h. Peak memory usage reached 10 GB during the evaluation phase, demonstrating the framework's efficiency and scalability for large-scale applications.  
The simulation setup supported up to:  
* 10,000 Ultra-Reliable Low-Latency Communications (URLLC) users;
* 50,000 Enhanced Mobile Broadband (eMBB) users;
* 500,000 Massive Machine-Type Communications (mMTC) devices.  
Key performance indicators (KPIs) included:
* Throughput: data rate aggregation for eMBB slices, critical for assessing network performance [11; 12].
* Latency: end-to-end latency for URLLC slices, essential for low-latency applications [13; 14].
* Resource utilisation: efficient management of physical network infrastructure [5; 8].  
Figure 6 illustrates the simulation setup. Data processing and analysis were divided between MATLAB (numerical computation and algorithm development) and Google Colab (machine learning execution). Simulink simulated dynamic traffic loads and network conditions, with results fed back for performance evaluation.