## 2 Edge Enabled Network Slicing
### Edge Enabled Network Slicing with Slice Isolation  
One of the common characteristics of the previously mentioned studies is that they did not take the isolation of resources into account during slicing which is also one of the key expectations of network slicing. Isolation can be defined as the ability to ensure that congestion, attacks, and lifecycle-related events on one network slice does not negatively impact other existing slices. In this respect, the use of a shared infrastructure makes isolation a key requirement of network slicing. Thus, slice isolation can further enhance end user experience for use cases that require low latency and high bandwidth for optimized device and application performance. Aiming to provide isolation among different services, Ref. [23] proposed a MEC slicing framework that allows network operators to instantiate heterogeneous slice services (e.g., video streaming, caching, and 5G network access) on edge devices. They pointed out that this is a challenging process as MEC resources are usually coupled. Hence, slicing one resource may lead to a performance degradation of another resource. To address this problem, they mathematically modelled coupling relationships among networking, storage and computation resources at each edge node by using collateral functions. Basically, collateral functions reflect whether the necessary resources are coupled and also determine the portion of different resources needed to be allocated on a given edge node. Their results are promising in a sense that they are able to instantiate slices six times more efficiently then state-of-the-art MEC slicing algorithms. Furthermore, their experimental results showed that they can simultaneously provide highly-efficient slicing of LTE connectivity, video streaming over WiFi, and video transcoding. [PERSON]. [34] posited that enabling resources isolation among slices is one of the most significant challenges for realizing softwarized base stations based on MEC. In this study, their aim is to isolate low latency slices as the primary concern and usage area of MEC is to enable low-latency applications. In this respect, they proposed an MEC-enabled slicing framework and proposed a novel slicing method for improving the isolation of low latency slices from the others. Their evaluation results showed that the proposed method can achieve minimal latency, even with a competing low-latency slice. [PERSON]. [35] took the isolation issue one step further and proposed a wireless network slicing solution considering both inter-slice isolation (i.e., no interference among slices), and intra-slice isolation (i.e., no interference between users in the same slice). In particular, they pointed out that in presence of selfish agents or data greedy applications, it is important for infrastructure providers to design an appropriate incentive plan to achieve and protect the social efficiency. Indeed, they use bidding algorithm where each slice, requested by network operators, needs to submit an individual bidding value. After receiving bids from different operators, the provider allocates resources to each bidding agent proportionally to their bidding values. After that, to provide fairness, they analysed optimal resource allocation under the Nash equilibrium and introduced a penalty value for each bidder. They mentioned that the key benefit of using such a resource bidding and allocation framework is that the network infrastructure providers do not know the true valuation of the network operators. Thus, isolation and fairness between slices can be provided by their proposed problem formulation. The proposed scheme achieves performance gain up to 13% in comparison to the equal sharing mechanism. Specifically for the mMTC use case, [PERSON]. [36] proposed an isolated network slicing approach for IoT applications. They considered the use case of a smart city required several different applications including air quality, temperature, traffic monitoring sensors, and smart buildings devices.