An Edge Resource Selection Scheme Considering QoS and Computational Parameters

Edge computing enhances cloud architectures through the integration of distributed platforms that operate closer to end-users. With dynamically changing network topologies, user mobility, multiple access networks, demand-driven service orchestration, and consequently several distributed service instances that are available, methods for selecting edge resources must adapt to these conditions. This consideration is particularly relevant for edge computing, where target applications often have strict Quality of Service (QoS) constraints. This work proposes an edge resource selection scheme that considers several QoS parameters, computing load, and service usage cost. These metrics are employed to derive a selection score through four distinct algorithms. The overall performance of each algorithm is evaluated by scoring examples, a sensitivity analysis, and in a system-level simulation of a fifth generation (5G) cellular network with user mobility and multiple Multi-Access Edge Computing (MEC) platforms. The results show that all approaches perform well under unambiguous QoS and load conditions, i.e., very good or very poor, but some lose accuracy under moderate, but still acceptable, conditions. For the goal of cost-optimized selection while meeting QoS constraints, one novel method presented is significantly superior to the others. Used in simulation, it generates 12% more computing load on cheaper platforms than its closest competitor, and maintains significantly lower loads on high-cost platforms.