Scaling and Offloading Optimization in Pre-CORD and Post-CORD Multi-Access Edge Computing

In 5G networks, multi-access edge computing (MEC) can be embedded into an access network (AN-MEC) and a core network (CN-MEC), which composes a two-tier MEC architecture for better scalability. In pre-Central Office Re-architected as a Data center (pre-CORD), AN-MECs are connected to a single but distant CN-MEC through Central Offices (COs). Disaggregation and virtualization of 5G network functions push CN-MEC into COs, which is known as post-CORD. Post-CORD has more CN-MECs closer to User Equipments than pre-CORD. In this work, we propose a scalable two-tier, multi-site, multi-server MEC architecture for pre-CORD and post-CORD. To adjust capacity and traffic allocation in such a distributed two-tier architecture, we integrate scaling and offloading with the objective of minimizing total capacity cost subject to the latency satisfaction percentage constraints, and solve the problem by Latency Aware Two-Phase Iterative Optimization (LA-TPIO). The results show that post-CORD with ten CN-MEC sites requires 30% less capacity than pre-CORD in satisfying 95% of URLLC traffic. Post-CORD utilizes about 48-77% less AN-MEC capacity than pre-CORD because post-CORD's aggregated but close-enough CN-MEC sites are ideal for serving URLLC traffic. Under heavy hotspot traffic, post-CORD's vertical and horizontal offloading percentages are 72% and 28%, respectively, while pre-CORD's are 99% and 1%, which means post-CORD introduces more horizontal offloading because it has links between not only AN-MEC sites but also CN-MEC sites to accommodate hotspot traffic.