Balancing the Edge: Multi-Service Request Allocation in Urban 5G-MEC Environment

In urban 5G Multi-access Edge Computing (MEC), overlapping server coverage and dense, heterogeneous user service demands often lead to uneven request distribution, causing resource underutilization at some edge servers and overloading at others. This imbalance degrades overall system efficiency, making load balancing essential for effective resource utilization and Quality of Service (QoS) assurance during multiple service placement and request allocation. We address this by formulating the Load-aware Service placement and Request allocation (LSR) problem as a multi-objective optimization model and proposing a scalable and efficient two-stage heuristic. The objective is to minimize server utilization variance while reducing service deployments and maximizing user coverage. In the heuristic, the first stage ensures maximum coverage through deterministic request allocation and greedy service placement, all within MEC constraints. The second stage iteratively refines service placement and request allocation to minimize utilization variance, offering reduced computational complexity compared to existing approaches. Experimental results demonstrate that our approach achieves significantly lower variance (65%) in optimal scenarios, reduces service deployments by 15%, increases user coverage by 15%, and establishes itself as an efficient centralized solution for quasi-static multi-service edge deployments in urban 5G-MEC environments.