Offloading Network Data Analytics Function to the Cloud with Minimum Cost and Maximum Utilization

Abstract

Cloud computing is being embraced more and more by telecommunication operators for on-demand access to computing resources. Knowing that 5G Core reference architecture is envisioned to be cloud-native and service-oriented, we propose, in this paper, offloading to the cloud, some of 5G delay-tolerant Network Functions and in particular the Network Data Analytics Function (NWDAF). The dynamic selection of cloud resources to serve off-loaded 5G-NWDAF, while incurring minimum cost and maximizing utilization of served next generation Node-Bs (gNBs) requires agility and automation. This paper introduces a framework to automate the selection process that satisfies resource demands while meeting two objectives, namely, cost minimization and utilization maximization. We first formulate the mapping of gNBs to 5G-NWDAF problem as an Integer Linear Program (ILP). Then, we propose a heuristic to solve it based on branch-cut-and-price technique combining all of branch-andprice, branch-and-cut and branch-and-bound. Results using pricing data from a public cloud provider (Google Cloud Platform), show that our proposal achieves important savings in cloud computing costs and reduction in execution time compared to other state-of-the-art frameworks.

Leveraging Network Data Analytics Function and Machine Learning for Data Collection, Resource Optimization, Security and Privacy in 6G Networks

“…In [83], the authors deal with use-case development interface (UDI), which defines the appropriate procedures among producers and consumers for their interactions, data exchange, as well as tracking the actions and evaluating the results in the context of 5G networks. The work in [84] deals with NWDAF offloading to the cloud, according to cost minimization and utility maximization. In this context, the mapping of gNBs to 5G-NWDAF has been formulated as an Integer Linear Program.…”