Fifth-Generation (5G) mobile cellular networks provide a promising platform for new, innovative and diverse IoT applications, such as ultra-reliable and low latency communication, real-time and dynamic data processing, intensive computation, and massive device connectivity. End-to-End (E2E) network slicing candidates present a promising approach to resource allocation and distribution that permit operators to flexibly provide scalable virtualized and dedicated logical networks over common physical infrastructure. Though network slicing promises the provision of services on demand, many of its use cases, such as self-driving cars and Google's Stadia, would require the integration of a Multi-Access Edge Computing (MEC) platform in 5G networks. Edge Computing is envisioned as one of the key drivers for 5G and Sixth-Generation (6G) mobile cellular networks, but its role in network slicing remains to be fully explored. We investigate MEC and network slicing for the provision of 5G service focused use cases. Recently, changes to the cloud-native 5G core are a focus with MEC use cases providing network scalability, elasticity, flexibility, and automation. A cloud-native microservices architecture, along with its potential use cases for 5G network slicing, is envisioned. This paper also elaborates on the recent advances made in enabling E2E network slicing, its enabling technologies, solutions, and current standardization efforts. Finally, this paper identifies open research issues and challenges and provides possible solutions and recommendations.

Multi-access Edge Computing (MEC) is a key enabler of the fifth-generation (5G) mobile cellular networks. MEC enables Ultra-reliable and Low-latency Communications (URLLC) by bringing the data and computational resources closer to the mobile users. As 5G deployments commence in earnest, researchers have turned their attention to various aspects of edge computing in an effort to leverage the new capabilities offered by 5G. In this paper, we propose the integration of Software Defined Networking (SDN) and cloud-native virtualization techniques, such as containers, with the MEC architecture, to facilitate the orchestration and management of Mobile Edge Hosts (MEH). The proposed architecture focuses on the endto-end mobility support required to maintain service continuity when mobile users relocate from one MEH to another. SDN is proposed as a reliable, programmatic paradigm to provide mobile edge orchestration and dynamic configuration of the underlying network for improved service continuity and quality of experience. The proposed architecture is validated through vehicle-to-everything simulations that highlight the advantage of the centralized network intelligence and the modularity and portability offered by SDN and containers. INDEX TERMS Software defined networking, mobility management, multi-access edge computing, 5G, cloud-native, containerization, URLLC.

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A power distribution network is a critical infrastructure in any society and any disruption has an enormous impact on the economy and daily lives. Therefore, the objective of this study is to transform the conventional power distribution systems into resilient autonomous microgrid networks by optimally sizing and siting the distributed generators (DGs). First, N main DGs are placed to transform an existing network into an autonomous microgrid network. Second, all the possible combinations of the initially deployed DGs are made and then the outage of 1 to N − 1 DGs is considered. Considering the outage of DGs in each combination (one at a time), the resiliency of the network is analysed. Amount of load shedding, total power loss in the network, and voltage limits are analysed in this step. Finally, based on the resiliency analysis, additional DGs are placed to enhance the resiliency of the transformed network. Heuristic methods (particle swarm optimisation and genetic algorithm) are used for both sizing and siting of DGs during the first and the second steps. The objective of the formulation is to minimise load shedding, total power loss (active and reactive), and voltage deviations in the network during DG outages. ω i weight function for velocity of particle i ω max , ω min min. and max. weights for velocity of particles k max , k maximum and current iteration numbers