# VNF-Enabled 5G Network Orchestration Framework for Slice Creation, Isolation and Management
## 2 Related Literature
### Motivation  
Table 1 summarizes the literature on network slicing components, such as slice creation, isolation, and management, and includes all related works. In addition, to aid comprehension, the table highlights the methodology, performance metrics, and work parameters used. The vast majority of papers use the same parameters and metrics to evaluate performance. It has been found that the literature differs depending on the methodology that was used as well as the network slicing components that were discussed. The majority of the works in the table provide solutions for slice isolation; however, a few of the works addressed slice creation on its own, and a few of the papers addressed slice management in addition to slice isolation. As a result, we determined that introducing an orchestration framework that addresses all three components of network slicing would be significant. In the proposed framework, individual components of network slicing are addressed using the most appropriate techniques. For this study, we made use of a machine learning technique called Random Forest to carry out the slice creation. After slice creation, we employed a PROMETHEE-II-based resource allocation scheme to provide better resource allocation to the requested service. Then, we use resource management to make sure that resources are used effectively, and also, we give priority to uRLLC services. The proposed framework's performance is evaluated based on the component it addresses. Precision, F1 Score, and recall are used to measure performance for slice creation via machine learning, whereas resource efficiency and acceptance rate are used to measure performance for slice isolation and management.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref. No.** & **Methodology Adopted** & **Components** & **Performance Metrics** & **Considered Attributes** \\ \hline
[14] & Reinforcement Learning & Slice creation & Learning Time and Revenue & Number of Resources in base station, base station capacity, Bandwidth, distribution \\ \hline
[15] & Machine Learning and Deep Learning & Deep & Slice creation & Accuracy & Device type, packet loss rate, speed, duration, bandwidth, jitter, delay rate \\ \hline
[16] & Deep Learning & Slice creation and isolation & Resource Efficiency and Violation Rate & Traffic, PRB usage and CPU load \\ \hline
[17] & Reinforcement Learning & Slice isolation & Power consumption & Number of sensors, traffic and monitoring devices \\ \hline
[32] & Deep Reinforcement Learning & Slice isolation & CPU and bandwidth utilization & CPU capacity, bandwidth, path length and number of requests \\ \hline
[19] & Dynamic Programming & Slice isolation & Bandwidth and fairness & Data rate, transmission power, link capacity and bandwidth \\ \hline
[22] & Dynamic Greedy Technique & Slice isolation & Residual energy consumption & Transmission power, distance, path loss and bandwidth \\ \hline
[24] & Complex Network Theory & Slice isolation & Resource Efficiency and Acceptance Ratio & CPU and bandwidth \\ \hline
[25] & VNE-NTANRC algorithm & Slice isolation & Resource Efficiency and Acceptance Ratio & CPU and bandwidth \\ \hline
[26] & Stackelberg game approach & Slice isolation & Power consumption and delay & Subchannels, devices, data rate and traffic \\ \hline
[27] & VIKOR approach & Slice isolation & Resource Efficiency and Acceptance ratio & CPU and bandwidth \\ \hline
[28] & Stochastic Network Principles & Slice isolation & Resource Efficiency & Delay rate, traffic rate, capacity and service type \\ \hline
[29] & UFLOP mechanism & Slice creation and isolation & Provisioning ratio and traffic allocation & CPU, bandwidth and delay time \\ \hline
[30] & SABA scheme & Slice isolation and management & Bandwidth consumption & Delay rate, bandwidth, link capacity and service type \\ \hline
[31] & FlexRAN controller & Slice isolation and management & RAM and CPU consumption & CPU, Memory and bandwidth utilization \\ \hline \hline \end{tabular}
\end{table}
Table 1: The literature Survey.