Dynamic Service Provisioning in the Edge-Cloud Continuum With Bounded Resources

We consider a hierarchical edge-cloud architecture in which services are provided to mobile users as chains of virtual network functions. Each service has specific computation requirements and target delay performance, which require placing the corresponding chain properly and allocating a suitable amount of computing resources. Furthermore, chain migration may be necessary to meet the services' target delay. We model and formalize the problem of finding a feasible chain placement and resource allocation, while minimizing the migration, bandwidth, and computation costs. We tackle this problem by partitioning it into a (i) CPU allocation problem, and a (ii) placement problem. For the CPU allocation problem, we find an optimal solution. For the placement problem, we show that even finding a feasible solution is NP-hard, and envision an algorithm that is guaranteed to find a feasible solution while leveraging a bounded amount of resource augmentation. Our algorithms are incorporated into a solution framework that aims to minimize both the cost and the required resource augmentation. The results, obtained through trace-driven, large-scale simulations, show that our framework can provide a close-to-optimal solution while running several orders of magnitude faster than an ILP solver.