## 2 Edge Enabled Network Slicing
### Edge Enabled Network Slicing without Slice Isolation
The proposed MEC-based architecture is basically a two-tier server structure in which a cloud-computing server resides in the first tier and some MEC servers reside in the second tier. Considering the availability of resources and demanded QoSs, necessary computing tasks can be processed directly on vehicles, be uploaded to MEC servers or to the cloud-computing server. For example, delay-sensitive applications, such as platooning management, dynamic HD map management, and other safety-related applications, are prioritized to be processed on MEC servers to decrease the latency. As a result of this work, they are able to achieve about 50% higher network throughput compared to other state-of-the-art schemes. [PERSON]. [31] also investigated network slicing for MEC systems. They realized that existing works are only focused on effective edge slicing from the perspective of mobile devices, therefore they aimed to maximize operator's revenue in a system where multiple requests had been made and no prior-knowledge of traffic distributions existed. Mainly, they developed a stochastic optimization algorithm that decides whether to accept a slice request that may need low-latency computation offloading based on sub-carrier assignments and CPU cycle frequency of the system and the amount of power allocation required to fulfil this request. The simulation results showed that their algorithm can achieve a balance between the revenue and the average delay, and can significantly increase the operator's revenue compared to existing schemes. Here, they compared their results with three main schemes which are a scheme that does not have slice request admission, a scheme that has a fixed channel allocation, and a scheme that only optimizes slice request admissions. Similarly, [PERSON]. [32] proposed a novel method of realizing E2E network slicing by jointly addressing the requirements of physical network and locations of users. In particular, their algorithm utilizes [PERSON]'s shortest path algorithm to choose the servers with fewer bandwidth usages for the placement of VNFs and to calculate a feasible path with a minimum bandwidth consumption. Their results showed that they are able to improve the load balancing by reducing the load of edge data centres. [PERSON]. [33] proposed a network slicing model for 5G mobile networks, including MEC, C-RAN (Cloud radio access network), and cloud data centre to provision QoS. Mainly, they utilized queueing theory to derive some QoS performance metrics such as the CPU utilization, throughput, drop rate, average number of message requests, average response time, and average waiting time. In addition, they highlight how to use the proposed network slicing model for the dynamic scalability of C-RAN/MEC cores. The researchers also gave significant illustrations on the impact of MEC nodes on the system delay by stating that the average response time without any MEC node is approximately nine times higher than with 10 MEC nodes. However, the average time response as a function of the MEC nodes saturates after a certain threshold. The systematic outcomes and experimental processes demonstrated that MEC nodes are important to provide low latency and the proposed slicing model has a robust capability to allocate the count of MEC and C-RAN cores required to attain the quality of service targets of 5G slices. Based on these studies, it can be seen that slicing the edge resources offer several performance and security advantages as it provides significantly lower latency as well as better scalability, coverage flexibility, and cost-efficiency. Leveraging the edge deployments, network operators can increase their operational efficiency with central slice management and can scale their solution to serve multiple customers.