Virtualized resource allocation is a key flexibility enabler towards future service-oriented 5G communication networks [1]. The virtualization of both the Core Network (CN) and Radio Access Network (RAN) must be such as to provide tenants (a.k.a. the slice owners) with the necessary resources that satisfy their Quality-of-Service (QoS) or even Quality-of-Experience requirements from physical Network Operators (NOs). The focus of this project is to investigate the allocation of virtualized network resources from an End-to-End (E2E) perspective, spanning both the CN and RAN where users reside and consume 5G services. The problem is highly practical (even though based on an interesting analytical background [2][3]) of major importance for future 5G services. The ultimate goal is to improve performance by moving or replicating applications, services or/and content with respect to the network edge points where user demand comes. Within this context, different types of resources like memory, storage, link bandwidth and/or latency, CPU cores and/or cycles, etc., have to be allocated together to form a new service node or for migrating an existing service node to another network point. Specific investigation goals This project is aimed at students interested in Machine Learning (ML) solutions for 5Gcognitive resource management. Within the context of this, the students will have the opportunity to Experience the design of resource allocation hybrid models inspired by reinforcement learning methods and, specifically, Q-learning [4] as well as congestion pricing/cost-based models along the lines of the work in [5][6]. Investigate, elaborate and evaluate the design of the hybrid models tailored for tackling multi-type resource allocation. Reinforcement Leaning (RL), either with Q-learning or-upon student's skills-Deep RL (DRL) Get in know important concepts that dominate the current and future 5G industry such as Multi-access Edge Computing (MEC) and resource Slicing in 5G.
X. S T Y L I A N O U Vasilako