## 1 Introduction
### Edge Computing  
The main idea of edge computing is to extend the capabilities of cloud servers to edge of networks by performing computationally-intensive tasks and storage at the closest point of interaction to fulfil low latency requirements [4]. As the long-distance transmission of data from end devices to the cloud servers incurs a great propagation delay, bringing the computational resources to the edge of the networks that is close to Radio Access Network (RAN) and User Equipment (UE) reduces latency and provides applications with real-time performance [5]. There are also several other advantages of processing data at the edge of the networks such as mitigating the threat of data leakage by eliminating the risk of single point of failure, minimizing the risk of traffic congestion, and providing scalability to the networks which makes edge computing one of the major enabler of 5G systems [6].  
In this respect, European Telecommunications Standards Institute (ETSI) formerly defined and standardized edge computing concept as Multi-Access Edge Computing (MEC) to allow the efficient and seamless integration of applications from vendors, service providers, and third-parties [7]. In their white paper, they defined the necessary specifications for MEC, provided reference architectures and recommendations, and also discussed some application scenarios such as massive sensor data analysis, active device location tracking and big mobile data. In addition to providing an execution environment for applications at the edge, MEC provides services with UE and RAN statistics, such as the radio channel quality of users and their location in the network, allowing us to build context-aware applications [8]. In particular, several 5G use cases are expected to rely on edge computing:  
1. Healthcare: Edge computing in healthcare focuses on capturing, analysing, and synthesizing of necessary information by effectively prioritizing critical traffic, accelerating computer-intensive operations such as compression and decompression of medical surgery images, eliminating performance overheads, and helping to improve security [9].
2. Video analysis: A major benefit of edge computing is to allow processing of video data within the end devices such as cameras, mobile phones, or vehicles that have processing power. This enhances the transition efficiency, reduces the network bandwidth load significantly, and allows end users to make faster decisions in critical situations [10].
3. Smart home and city: Edge computing can be useful to manage and orchestrate devices for smart homes and cities by reducing response times of these devices by performing computations and data caching locally [11].  
Although edge computing reduces latency, offers low operating costs, and increases consumer satisfaction, it also brings new challenges in the field of privacy and energy efficiency [12]. Enormous real-time data collection from mobile devices, and the challenge of storing and processing them in edge servers could potentially lead to a violation of the security and confidentiality of the data. In addition to that, continuous collection and transmission of data between mobile devices and edge servers consumes a huge amount of energy which introduces an energy efficiency challenge, as mobile devices are often powered by batteries. Thus, such issues should not be overlooked while designing edge sliced network solutions.