## 1 Introduction  
Beyond 5G (B5G) and 6G networks are envisioned to meet a plethora of service requirements supporting high resource and technology heterogeneity, while adopted architectural paradigms, such as Centralized-Radio Access Network (C-RAN) and Network Function Virtualization (NFV), offer the necessary high flexibility and configurability to the network. The increasing use of NFV, in particular, to \"software\" functions and applications [1, 2, 3] (i.e., decouple the function logic from the underlying hardware running the actual code) in numerous Open System Interconnection (OSI) layers has transformed the majority of \"traditional\" network functions (e.g., Network Address Translation (NAT), firewall, load balancing, and Intrusion Detection/Prevention System (IDPS)) into Virtual Network Functions (VNFs), which can run on generic computational infrastructure that can be dynamically deployed. Although we use the generic term \"Central Processing Unit (CPU) resources\" to refer to this computational infrastructure, our model and ensuing analysis can capture any type of computational device, including Graphics Processing Units (GPUs), Field Programmable Gate Arrays (FPGAs) and other computational acceleration platforms. The NFV-supporting physical computational infrastructure contains a number of nodes, including traffic-forwarding switches and computational nodes of different capabilities that can host VNFs in the form of Virtual Machines (VMs), containers, or unikernels. Although traditional cellular architectures were mainly equipped with cloud servers, located at distant locations offering high computational power at low cost, the need for supporting ultra-low latency B5G services has motivated Multi-Access Edge Computing (MEC) [4]. To this end, MEC nodes offer computational capabilities very close to the user, often being attached to Base Stations (BSs), and are able to achieve ultra-low latency at the expense of high cost, thus leading to a nontrivial cost/distance tradeoff that needs to be quantitatively examined. The above flexible deployment introduces a new exploitable \"degree of freedom\" regarding the potential location of the deployed VNFs (referred to as the \"VNF placement problem\" [5; 6]) but also introduces new challenges to traditional user association and traffic routing problems, since these problems are strongly coupled with the location of the nodes running the VNFs. Furthermore, the VNFs themselves typically operate in synergy with each other to form Service Function Chains (SFCs), i.e., ordered sequences of VNFs which process packets in an End-to-End (E2E) manner, within the operator's network, and according to specific rules matching a given service request. This ordering adds a crucial constraint to the routing problem, as the SFC is considered to successfully meet the service request **only** when the selected routed path passes through the VNFs comprising the specific SFC in exactly the order specified; for example, if an SFC is described by the ordered sequence NAT\(\rightarrow\)Firewall\(\rightarrow\)NAT, then all packets belonging to this SFC must be processed by only these three VNFs in the specified order. The above discussion motivates the need to jointly study VNF placement alongside computational and communication resource allocation in a mobile network, while satisfying the SFC, throughput, and delay requirements [7; 8; 9]. In addition, B5G is expected to include a variety of different access and transport technologies with distinct characteristics that should be jointly studied. Specifically, apart from fiber links interconnecting the physical nodes, wireless links may also be deployed as an X-haul transport solution, to offer high flexibility close to the Access Network (AN) [10]. Millimeter wave (mmWave) constitutes a very promising candidate to serve this purpose, due to its high bandwidth availability and antenna gains that are able to compensate for the higher path loss in this band.