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.
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
Proportional fair scheduling (PFS) is a widely used scheduling algorithm in the wireless networks which utilizes the network resources efficiently while maintaining a balance with the fairness among the users. It is a channel aware scheduling algorithm that allocates resources to the users with the best channel conditions, it also takes into account the users with bad channel quality by considering the user's access history in the scheduling decision. In this paper, the authors propose a two-step multiple carrier proportional fair scheduling (MC-PF) algorithm for the cloud radio access networks (C-RAN). The main purpose of this algorithm is to maximize the sum of logarithm transmission rate. The scheduler assigns users to each carrier in order to maximize the logarithm transmission rate for overall links of C-RAN. To prove the effectiveness of the proposed algorithm, the authors have shown the simulation results and compared it with the round robin (RR) scheduling scheme.
<p>Network slicing is a key enabler for 5G and beyond networks that permits operators to provide scalable, flexible, and dedicated networks over a common physical infrastructure. To cope with the rising demand for Ultra-Reliable and Low-Latency Communication (URLLC) in beyond 5G networks, the provision of dedicated secure networks closer to the users is essential. Multi-access Edge Computing (MEC) is a promising technology that provides data and computational resources closer to mobile users. However, MEC servers are resource-constrained, and offering dedicated service-specific network slices at the edge in a highly dynamic and mobile environment is challenging. Network slicing and MEC are being evolved by two different standardization bodies that limit their integration and raise mobility challenges that deserve more attention. We propose a cloud-native microservices architecture for network slice mobility management in MEC that permits each MEC slice to be distributed as stateless and independently deployable microservices. The proposal separates the MEC slice operational data and the user context, as each network function in an MEC slice stores the context in a separate shared database. The proposed architecture leverages new SDN extended federation modules in compliance with the ETSI requirements for inter-MEC system coordination. The federation modules support a more flexible and scalable creation of network slices at MEC servers, efficient resource utilization, and mobility of network slices across MEC servers. The simulation results show that our proposed architecture outperforms the existing SDN-based approaches for network slicing in MEC by achieving high slice acceptance rates and reduced slice migration delay.</p>
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