# VNF-Enabled 5G Network Orchestration Framework for Slice Creation, Isolation and Management
###### Abstract  
Network slicing is widely regarded as the most critical technique for allocating network resources to varied user needs in 5G networks. A Software Defined Networking (SDN) and Network Function Virtualization (NFV) are two extensively used strategies for slicing the physical infrastructure according to use cases. The most efficient use of virtual networks is realized by the application of optimal resource allocation algorithms. Numerous research papers on 5G network resource allocation focus on network slicing or on the best resource allocation for the sliced network. This study uses network slicing and optimal resource allocation to achieve performance optimization using requirement-based network slicing. The proposed approach includes three phases: (1) Slice Creation by Machine Learning methods (SCML), (2) Slice Isolation through Resource Allocation (SIRA) of requests via a multi-criteria decision-making approach, and (3) Slice Management through Resource Transfer (SMART). We receive a set of Network Service Requests (NSRs) from users. After receiving the NSRs, the SCML is used to form slices, and SIRA and SMART are used to allocate resources to these slices. Accurately measuring the acceptance ratio and resource efficiency helps to enhance overall performance. The simulation results show that the SMART scheme can dynamically change the resource allocation according to the test conditions. For a range of network situations and Network Service Requests (NSRs), the performance benefit is studied. The findings of the simulation are compared to those of the literature in order to illustrate the usefulness of the proposed work. 5G network; network slicing; resource allocation; machine learning +
Footnote †: journal: Sensor and Actuator Networks  
is offered that takes into account the costs of deployment, energy, traffic, and propagation delay. VNF insertion in large-scale systems has been addressed by the development of WiNE (Wireless Network Embedding) [7]. In order to solve the VNF placement problem, WiNE considers three distinct scenarios: linear requests, branching requests, and requests with loops. In [8], we find an in-depth analysis of SDN and VNF network slicing, including their foundational concepts, designs, and problems. Providing network slices is difficult for many reasons, such as maximising resource efficiency, maximising acceptance ratio, maximising data confidentiality, maximising latency, and supporting a wide diversity of user needs [9]. Network slicing, as is well known, entails three key responsibilities: slice creation, isolation, and management. Many of the network slicing orchestration frameworks concentrate on one of the single duties of network slicing. It is recognized that there is still a requirement to build the orchestration framework to provide each service for all three network slicing duties. Furthermore, when a single framework provides services in all three elements of slicing, VNF managers have a competitive advantage in offering higher-quality services to their clients. By consideration of the problems previously described in the existing research, this investigation presents an orchestration architecture that takes care of all three network slicing responsibilities. The main objectives and findings of our study are as follows:  
* To model a network orchestration framework that combines network slice generation, isolation, and management to provide efficient and simple network slicing. * To identify the best machine learning approach for slice creation where the performance dataset is prepared based on the realistic characteristics of the use cases. * To achieve the best possible distribution of physical infrastructure (PI) resources among Network Service Requests (NSRs), it is necessary to develop a mechanism for isolating network slices. The combination of Multi Criteria Decision Making (MCDM) and Shortest Path Algorithms are utilised for the implementation.