Quantifying the Gain of Dynamic Network Slicing under Stringent Constraints

By abstracting network resources and functions, network slicing allows to partition a single physical infrastructure into multiple logical independent networks, called slices, that can be tailored to the diverse needs of a wide range of application domains. In order to be independently managed by different players (tenants), slices need to be logically isolated from each other. As some of the applications envisioned for 5G networks are quite critical from performance point of view, a static slicing of resources, where isolation is not only logical but also physical, may appear a natural solution. However, the price to pay in terms of resource overprovisioning can be high, and therefore dynamic allocation strategies, based on optimized resource sharing, are alternative solutions. In this work, we propose an optimization framework to compare the two resource allocation schemes and define a metric to quantify the slicing gain. We show that the dynamic slicing outperforms the static approach and that the gain is significant even for stringent performance requirements, making dynamic slicing a viable solution also for critical applications.