Dynamic VNF Placement, Resource Allocation and Traffic Routing in 5G

TL;DR

This paper addresses the complex problem of dynamically managing VNF placement, resource allocation, and traffic routing in 5G networks to maximize operator profit, considering real-world constraints and request variability.

Contribution

It introduces a joint optimization framework and a practical heuristic for dynamic VNF placement and resource management in 5G, accounting for setup times and real-world constraints.

Findings

Heuristic outperforms existing online approaches.

Heuristic achieves near-optimal performance.

Effective in large-scale, real-world scenarios.

Abstract

5G networks are going to support a variety of vertical services, with a diverse set of key performance indicators (KPIs), by using enabling technologies such as software-defined networking and network function virtualization. It is the responsibility of the network operator to efficiently allocate the available resources to the service requests in such a way to honor KPI requirements, while accounting for the limited quantity of available resources and their cost. A critical challenge is that requests may be highly varying over time, requiring a solution that accounts for their dynamic generation and termination. With this motivation, we seek to make joint decisions for request admission, resource activation, VNF placement, resource allocation, and traffic routing. We do so by considering real-world aspects such as the setup times of virtual machines, with the goal of maximizing the mobile network operator profit. To this end, first, we formulate a one-shot optimization problem which can attain the optimum solution for small size problems given the complete knowledge of arrival and departure times of requests over the entire system lifespan. We then propose an efficient and practical heuristic solution that only requires this knowledge for the next time period and works for realistically-sized scenarios. Finally, we evaluate the performance of these solutions using real-world services and large-scale network topologies. {Results demonstrate that our heuristic solution performs better than a state-of-the-art online approach and close to the optimum.