Multi-objective 5G-MEC service relocation: A joint view on performance, availability, and privacy

Multi-access Edge Computing (MEC) is a key enabler of low-latency services in 5G-and-beyond networks, offering computing resources closer to users. However, user mobility may require frequent service relocations, which introduce challenges in maintaining service performance, availability, and user location privacy. Existing relocation strategies often focus on individual metrics, such as latency or service disruptions, but fail to address the inherent trade-offs between these objectives and the emerging privacy risks associated with user trajectory tracking. In this paper, we formulate a service relocation problem that simultaneously considers latency violations, service disruptions, and user location privacy. We propose the Multi-Objective Resource Allocation (MORA), a scoring-based heuristic that balances these conflicting factors through an adaptive scoring system. MORA dynamically determines the optimal timing for relocation and selects the most suitable MEC Host (MEH) to minimize latency violations, service disruptions, and privacy risks. We evaluate MORA across diverse scenarios, including varying user loads, percentages of stateless vs. stateful services, MEH distributions, and weight configurations. Our results demonstrate that MORA achieves a balanced trade-off across all metrics.