Network-Aware Placement Optimization for Edge Computing Infrastructure Under 5G

Edge Computing has grown into a key solution for coping with the stringent latency requirements of 5G scenarios. Nevertheless, the edge node placement problem raises critical concerns regarding deployment and operational expenditures (i.e., mainly due to the number of nodes to be deployed), current backhaul network capabilities, non-technical placement limitations, etc. In this paper, a novel framework called EdgeON is presented aiming at reducing the overall expenses when deploying and operating an Edge Computing (EC) network, taking into account the usage and characteristics of the in-place backhaul network. The framework implements several placement and optimization strategies targeting the heavily constrained network-aware Edge Node Placement Problem (ENPP). The results obtained by our solution are promising, achieving an average of 30% less Edge Nodes (ENs) deployed and 25% higher average usage ratio when compared to other widely used heuristics. Furthermore, our strategy achieved a score offset of less than 2% in comparison to the implemented Mixed Integer Linear Programming (MILP).