The rapid transformation of mobile network infrastructures from hardware-based systems to software-defined networks (SDNs) has introduced several novel paradigms that aim to enhance scalability, flexibility, and efficiency. One of the most significant advancements in this shift is the concept of network slicing. In essence, network slicing allows a single physical network to be divided into multiple virtual networks, each tailored to the specific requirements of different use cases. These slices can vary in terms of bandwidth, latency, security, and resource allocation, making them highly adaptable to different industries, such as autonomous vehicles, smart cities, and Internet of Things (IoT) applications. This capability offers unprecedented flexibility for service providers, as they can deploy and manage various services on the same physical infrastructure without the need for expensive and complex hardware upgrades.

However, realizing the full potential of network slicing presents several challenges. One of the key issues is the complex management of multiple network slices that may have conflicting requirements. For example, ensuring ultra-reliable low-latency communication for one slice while maximizing bandwidth for another slice can be difficult, especially when they share the same underlying physical resources. The dynamic nature of network slicing, where slices are created, modified, and terminated on demand, also requires highly efficient orchestration and automation systems. Furthermore, maintaining security and isolation between slices is essential to prevent interference or data breaches. As mobile networks continue to evolve, addressing these challenges will be crucial to fully unlocking the benefits of network slicing and realizing the vision of highly flexible, software-driven networks.

This thesis explores innovative approaches to improve the profitability of Mobile Network Operators (MNO) through advanced network slicing strategies in 5G networks. The research focuses on three key areas: overbooking network slices for maximize financial gains, cost-efficient network slice management, and operational performance of Mobile Network Aggregators (MNA) within Network Slicing as a Service (NSaaS) context under roaming scenarios. These innovations leverage state-of-the-art techniques, including deep learning, classical optimization, and data-driven algorithms, to tackle the complex challenges of resource provisioning, network function lifecycle management, and global service optimization.

This document delves into these advancements, structured across several chapters that explore these key areas in depth. In the first contribution, we explore the NSaaS concept and introduce slice overbooking as a promising strategy to maximize resource utilization and boost net profit in cloud-native mobile networks. Network slicing enables the creation of virtualized, custom-tailored network slices, but managing these slices efficiently remains a challenge. Overbooking allows network operators to admit more slices than available physical resources by capitalizing on the fact that tenants seldom use their total reserved capacity simultaneously. The chapter presents a complete NSaaS management solution, overbooKing-Aware Network Slicing as a Service (\name), which integrates deep learning with classical optimization to address the dual problems of admission control and resource allocation. Through extensive experimentation with large-scale real-world data on tenant demands, the results show that \name boosts operator profits potentially multiplying it by four compared to non-overbooking strategies under real-world~~conditions.

In the second contribution, the focus shifts to zero-touch management systems, which promise autonomous network operation with minimal human intervention. As modern network infrastructures have evolved into systems with numerous virtualized network functions, traditional manual approaches to lifecycle management are increasingly inadequate. In response, this chapter introduces \azext, a data-driven solution for anticipatory resource provisioning in network slicing environments. Using a hybrid and modular deep learning architecture, \azext forecasts future service demands and determines optimal trade-offs between resource provisioning, instantiation, and reconfiguration costs and performance requirements.

Tested on a large-scale network, AZTEC+ outperformed existing state-of-the-art management strategies by up to 5.85 times, proving its effectiveness in reducing network costs and addressing the complexity of virtualized mobile networks. It effectively balances costs associated with resource instantiation and reconfiguration, making it a highly efficient solution for managing dynamic network slices autonomously. This chapter emphasizes how zero-touch management, paired with anticipatory resource provisioning, offers a scalable approach to future network management.

In the third contribution, we explore the added complexity introduced by MNAs, which represent a new frontier in global mobile telecommunications in NSaaS. MNAs, such as Google Fi, Twilio, and Truphone, operate by leveraging multiple MNOs to provide mobile communication services across different regions. Unlike traditional MNOs, which are limited by geographic boundaries, MNAs dynamically connect to the MNO that offers the best performance based on location and time, ensuring optimal service quality for users, especially those frequently crossing borders. However, the dynamic nature of MNAs introduces a new layer of complexity in meeting network slicing’s Quality of Service (QoS) guarantees as the isolation and management of slices become more intricate with the involvement of multiple, regionally dispersed MNOs. To address this, we quantify and compare the performance of MNA-driven models against traditional MNOs, offering insights into the challenges and trade-offs in achieving reliable QoS in these advanced operator frameworks.

This section presents a detailed performance analysis of the three aforementioned MNAs, comparing their performance for key applications like web browsing and video streaming within NSaaS across diverse geographical regions, namely the USA and Spain. While MNAs may introduce slight delays compared to local MNOs in certain regions, they significantly outperform the traditional home-routed roaming model in terms of service quality. Moreover, emulation studies using open-source 5G implementations deployed across Amazon Web Services (AWS) locations illustrate the performance gains MNAs can achieve through advanced network function virtualization. This chapter highlights the potential of the MNA model to reshape global mobile services, offering more flexible, efficient, and seamless experiences for end-users.

Overall, this research provides valuable insights into NSaaS overbooking management, zero-touch resource provisioning, and the global reach of MNAs. Together, these innovations represent significant strides toward realizing the next generation of mobile networks, where resource efficiency, automation, and global service quality are paramount. The research highlights the economic benefits of slice overbooking, demonstrating how operators can significantly increase profitability by intelligently managing their resources. Furthermore, by integrating hybrid models of AI and optimization, it lays a strong foundation for future developments in network slicing and cost optimization, offering practical implications for both MNO and application developers. As 5G networks continue to evolve, this work sheds light on the shifting landscape of global network operators and provides a roadmap for addressing the growing complexities of resource allocation and service management in a cloud-native, AI-driven environment. Through a blend of deep learning, anticipatory algorithms, and network virtualization, the telecommunications industry is better positioned to meet the ever-evolving demands of users while optimizing network performance across a wide range of dynamic environments.

About Sergi Alcalá

Sergi Alcalá-Marín is from Barcelona. He did a bachelor’s degree in Telecommunication Systems Engineering at UPC (Polytechnical University of Catalunya). He did a master’s in advanced Telecommunication Technologies with a mention in Wireless Networks, also at Polytechnical University of Catalunya. He worked at Barcelona University as Assistant Researcher and Event Manager for 3 years and organized the 14th and 15th International workshop in Magnetism and Superconductivity at the Nanoscale for Barcelona University. He also worked as Network Engineer at Mobile World Congress 2019. He is interested in Network Performance, Mobile Communications and Machine Learning. He is part of the Network Data Science Group of IMDEA as PhD Student.

PhD Thesis Advisor:  Dr. Albert Banchs & Dr. Marco Fiore, IMDEA Networks Institute, Madrid

University: Universidad Carlos III de Madrid

Doctoral Program: Telematic Engineering

PhD Committee members:

• President: Vincenzo Mancuso, Part-time Research Associate Professor, IMDEA Networks Institute
• Secretary: Chrysa Papagianni, Assistant Professor, University of Amsterdam
• Panel member: Evgenia Christoforou, Research Associate (Fairness and Ethics in AI – Human Interaction Group) at the CYENS-Centre of Excellence, Nicosia, Chipre