Publication Date

Spring 2024

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)


Computer Science

First Advisor

Genya Ishigaki

Second Advisor

Katerina Potika

Third Advisor

Fabio di Troia


Dynamic Pricing, Deep Reinforcement Learning, Revenue Maximization, Network Slicing.


Network slicing is a key enabler of next-generation networking that supports a diverse array of network applications with different service requirements. In particular, elastic network slicing that dynamically scales the bandwidth reserved for each network slice would benefit both slice users and providers through cost-effective resource utilization. However, the elasticity poses a complex problem of managing the dynamics of fluctuating network slices. It is necessary for a slice provider to maintain the balance of different types of slice requests, so it can accommodate more requests while satisfying the service requirements for each slice type. Dynamic pricing of slice resources is a way for a network operator to realize the ideal balance of different types of network slices by implicitly communicating the current network state to slice users. In this project, we formulate an online pricing scheme for elastic network slices, which maximizes the revenue of slice providers. Our problem considers (1) slice users’ a priori preference over different types of network slices (susceptibility to Service Level Agreement violations) and (2) the influence of prices on slice users’ decision to choose a type of slice services. Our simulation experiments in a practical network topology demonstrate the revenue increase of a network operator by encouraging the use of elastic network slices.

Available for download on Friday, May 23, 2025