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Multi-Access Edge Computing (MEC) promises to provide sufficient computing capacity close to users and realize smart management at the edge of mobile network. To achieve the aforementioned objectives, it is indispensable to implement real-time monitoring of the whole MEC network. However, the geo-distributed deployment of MEC infrastructure dramatically increases the communication latency of gathering the state information (e.g. current status and resource utilization) from servers at the edge. This paper addresses latency-optimal placement and autoscaling of monitoring functions in MEC. First, we formally formulate latency-optimal placement of monitoring functions as an integer linear programming problem and proposed a genetic algorithm-based meta-heuristic to obtain the optimal solution with fast convergence. Moreover, to serve the time-varying demand on resource capacity from diversified mobile services, an online VNF scaling scheme is designed for realizing on-demand resource allocation. The effectiveness of our heuristic algorithm is verified through both numerical simulation and experiments in real cloud environment. Experimental results demonstrate performance superiority of the proposed approach over the state-of-art researches, in terms of algorithm CPU time, total network latency and long-term scaling cost.

Toward Latency-Optimal Placement and Autoscaling of Monitoring Functions in MEC