VNF Placement and Resource Allocation for the Support of Vertical Services in 5G Networks

TLDR

5G networks aim to support diverse industry verticals by offering services with varied performance needs, yet mapping these vertical requirements to network infrastructure decisions such as VNF placement, resource allocation, and traffic routing remains a critical challenge. The study seeks to jointly determine VNF placement, resource assignment, and routing while accounting for their mutual interactions in an efficient manner. A queuing‑based model is formulated and employed by the network orchestrator to optimally match vertical requirements to available resources, and a fast solution strategy called MaxZ is proposed to reduce computational complexity. Performance evaluation across realistic scenarios demonstrates that MaxZ substantially outperforms state‑of‑the‑art alternatives and achieves results consistently close to the optimum.

Abstract

One of the main goals of 5G networks is to support the technological and business needs of various industries (the so-called verticals), which wish to offer to their customers a wide range of services characterized by diverse performance requirements. In this context, a critical challenge lies in mapping in an automated manner the requirements of verticals into decisions concerning the network infrastructure, including VNF placement, resource assignment, and traffic routing. In this paper, we seek to make such decisions jointly, accounting for their mutual interaction, and efficiently. To this end, we formulate a queuing-based model and use it at the network orchestrator to optimally match the vertical's requirements to the available system resources. We then propose a fast and efficient solution strategy, called MaxZ, which allows us to reduce the solution complexity. Our performance evaluation, carried out accounting for multiple scenarios representative of real-world services, shows that MaxZ performs substantially better than state-of-the-art alternatives and consistently close to the optimum.