Anticipatory Resource Allocation and Trading in a Sliced Network

Dynamically sharing network resources in a sliced multi-tenant network can provide cost efficient solutions that are able to guarantee specific service requirements for 5G networks and beyond. By automatizing the negotiations between tenants and infrastructure providers over the shared resources, it is possible to maximize the flexibility of the network in a very short time frame, thus increase efficiency. However, negotiating resources in a reactive manner can bring risks to the tenants due to traffic variations, and can also limit the gain in terms of spectral efficiency for the infrastructure provider. In this paper, we focus on how to exploit anticipatory strategies relying on predicted information on users' conditions in order to improve the efficiency of the proposed dynamic network slicing and trading framework. In particular, we analyze how to integrate a prediction algorithm into our scheme and analyze the techno-economic impacts of the anticipatory approach. Finally, we introduce a novel filtering algorithm to limit the impacts of prediction errors. Our results prove that using anticipatory strategies in dynamic negotiations and resource allocation increases tenants' utilities, while allows the infrastructure provider to accommodate more requests.