Publication Date
Spring 2024
Degree Type
Master's Project
Degree Name
Master of Science in Computer Science (MSCS)
Department
Computer Science
First Advisor
Genya Ishigaki
Second Advisor
Katerina Potika
Third Advisor
Fabio di Troia
Keywords
Dynamic Pricing, Deep Reinforcement Learning, Revenue Maximization, Network Slicing.
Abstract
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.
Recommended Citation
Jaison, Jovian Anthony, "Reinforcement Learning-based Dynamic Pricing for Revenue Maximization with Elastic Network Slicing" (2024). Master's Projects. 1386.
DOI: https://doi.org/10.31979/etd.hya5-mvhs
https://scholarworks.sjsu.edu/etd_projects/1386
