Abstract
The emerging and foreseen advancements in 5G and Beyond 5G (B5G) networking infrastructures enhance both communications capabilities and the flexibility of executing computational tasks in a distributed manner, including those encountered in edge computing (EC). Both, 5G & B5G and EC environments present complexities and dynamicity in their multi-level and multimodal infrastructures. DDDAS-based design and adaptive and optimized management of the respective 5G, B5G, and EC infrastructures are needed, to tackle the stochasticity inherent in these complex and dynamic systems, and to provide quality solutions for respective requirements. In fact, both emerging communication and computational technologies and infrastructure systems can benefit from their symbiotic relationship in optimizing their corresponding adaptive and optimized management. EC enabled by 5G (and future B5G) allows efficient distributed execution of computational tasks. DDDAS-based methods can support the adaptivity in bandwidth and energy efficiencies in 5G and B5G communications. On the other hand, EC has become a very attractive feature for critical infrastructure such as energy grids as it allows for secure and efficient real-time data processing. In order to fully exploit the advantages of EC, the communication network should be able to tackle the changing requirements related to the task management within edge servers. Thus, leveraging the DDDAS paradigm, we jointly optimize the scheduling and offloading of computational tasks in an EC-enabled microgrid considering both physical constraints of microgrid and network requirements. The results showcase the superiority of the proposed DDDAS-based approaches in terms of network utilization and operational efficiencies achieved, with microgrids as case example.