Macroeconomics

Context (archaeology)

World Wide Web

Process (computing)

Service (business)

Monetization

Biology

Statistics

Markov process

Service provider

Accounting

Operating system

Economics

Computer science

Markov decision process

Slicing

Distributed computing

Business

Paleontology

Mathematics

Revenue

Economy

Virtual network

IEEE Transactions on Mobile Computing

It is now commonly agreed that future 5G Networks will build upon the network slicing concept. The ability to provide virtual, logically independent “slices” of the network will also have an impact on the models that will sustain the business ecosystem. Network slicing will open the door to new players: the infrastructure provider, which is the owner of the infrastructure, and the tenants, which may acquire a network slice from the infrastructure provider to deliver a specific service to their customers. In this new context, how to correctly handle resource allocation among tenants and how to maximize the monetization of the infrastructure become fundamental problems that need to be solved. In this paper, we address this issue by designing a network slice admission control algorithm that (i) autonomously learns the best acceptance policy while (ii) it ensures that the service guarantees provided to tenants are always satisfied. The contributions of this paper include: (i) an analytical model for the admissibility region of a network slicing-capable 5G Network, (ii) the analysis of the system (modeled as a Semi-Markov Decision Process) and the optimization of the infrastructure providers revenue, and (iii) the design of a machine learning algorithm that can be deployed in practical settings and achieves close to optimal performance.

A Machine Learning Approach to 5G Infrastructure Market Optimization