## 3 Proposed Optimal Orchestration Process
### Framework Overview and Technical Contributions
#### 3.1.2 Optimal Network Slice Service Orchestration (ONSSO)  
The ONSSO framework implementation focuses on three critical aspects: monitoring granularity, orchestration efficiency, and policy enforcement. The real-time monitoring system operates on a 100 ms sampling interval, providing highly granular insights into network performance. This sampling rate was chosen based on extensive testing that showed it provides an optimal balance between monitoring accuracy and system overhead. The monitoring system tracks key performance indicators, including latency (achieving consistent sub-10 ms measurements for URLLC slices), throughput (varying based on slice type, with eMBB slices achieving up to 20 Gbps), and resource utilisation efficiency (maintaining 90% average utilisation across active slices).  
The orchestration engine employs a novel approach to network slice template generation, utilising machine learning models trained on historical performance data. This approach has demonstrated a 40% reduction in slice deployment time compared to traditional template-based approaches, while maintaining higher accuracy in resource allocation predictions. The service function chaining algorithm achieves optimal function placement, with an average chain setup time of 200 ms, representing a 30% improvement over conventional chaining methods. Figure 3 illustrates the architectural overview of the proposed framework, while Figure 4 depicts the relationship between the eSS module pipeline and the proposed new vertical CNaaS framework, with the eSS module implemented within the CNaaS. Figure 3 highlights the components spanning from data ingestion to network topology. The data, which can be live, historical, generated by AI, or a combination of these, is fed into the eSS model pipeline. This pipeline preprocesses the data, analyses it, and identifies the requirements for the network slice template description.  
The framework handles varying types of network data and adapts to different data sources. It provides mechanisms to preprocess and clean the monitored traffic data, evaluates required resources for each application datum, and formulates the network slice template for network slice orchestration. The flexibility of LazyPredict allows the framework to incorporate new algorithms as they become available. Users can update the existing Python library to include new algorithms, which the framework will then automatically  
Figure 3: System components of the proposed framework architecture diagram.  
evaluate, ensuring continued relevance and adaptability. Figure 4 shows the eSS module pipeline within the CNaaS framework. Metrics such as the service requirements, business goals, and policies are fed into the pipeline. The eSS pipeline then outputs a bespoke slice or a standard 5G network slice depending on available network resources.  
Our validation methodology combined theoretical analysis with practical implementation using Google Colab for machine learning model training and MATLAB/Simulink for network simulation. The integration between these platforms was achieved through a custom API that enabled real-time data exchange and validation.  
The MATLAB/Simulink environment was configured to simulate various network conditions, including:  
* High-load scenarios with multiple competing slices;
* Dynamic traffic patterns mimicking real-world enterprise networks;
* Resource constraint situations to test scaling capabilities;
* Multi-tenant environments with varying QoS requirements.