**1. Introduction**

Connected vehicles can provide a large set of services for smarter and safer mobility. As an example, a problem highly felt worldwide is road safety [1] where vehicular networks can help in providing prompt information to drivers and alerting possible dangerous situations by allowing vehicles to communicate with each other. It is possible to distinguish between short-range direct communications and long-range network communications [2,3]. Different communications need different network requirements, such as low latency, high computational capacity, and high reliability, depending on the application [4].

To provide the aforementioned services, 5G networks can be used to carry data to/from vehicles and road infrastructure. Centralized cloud-based Radio Access Networks (C-RANs) represent an effective solution to design high-capacity radio access in 4G and 5G networks and to support challenging use cases [5], such as the ones of vehicular networks. C-RAN introduces unprecedented flexibility by efficient application of Network Function Virtualization (NFV) [6] jointly with Software Defined Networking (SDN) [5,7,8]. SDN can, in fact, provide suitable control and managemen<sup>t</sup> support to optimally locate virtualized network functionalities to intelligent nodes in the cost and power efficiency perspectives. This is of particular importance when considering highly dynamic and performance-constrained contexts as happens in 5G networking. In addition, to ensure timely network adaptation to user needs, SDN control and managemen<sup>t</sup> must cope with a potentially high

number of network elements and, consequently, the design of control algorithms calls for highly scalable approaches. Virtualized baseband functionalities are suitably located and centralized in the nodes of the optical transport network implementing a C-RAN for enhanced functionality and cost-optimization purposes [7]. The nodes hosting these pooled virtual baseband units (BBUs) are called BBU hotels. BBU hotels can be provided with an additional computational capacity to perform time-sensitive operations required by low-latency services, as per the Multi-access Edge Computing (MEC) paradigm [9]. MEC, by providing 5G with processing resources at the network edge, allows to achieve stringent application requirements. However, widespread deployment of these nodes may be costly; therefore, intelligent nodes hosting BBU hotels and edge computing resources need to be identified in relation to latency and processing constraints. Moreover, the problem of BBU hotel placement in C-RAN has been shown to be NP-hard [10], requiring novel strategies to make optimal approaches more scalable.

This paper proposes an Integer Linear Program (ILP) to solve the joint deployment problem of baseband processing and edge computing with reliability against single-node failure in C-RAN. The main objective of this strategy is to minimize the nodes in which processing capabilities must be installed while ensuring latency and optical link (i.e., maximum wavelengths over fibers) constraints are not violated. To overcome the computational complexity of classical optimization approaches, a hybrid (based on both heuristic and ILP) deployment strategy is also proposed. The algorithm performs a first phase in which the initial set of nodes candidate to host baseband and edge computing functions is reduced and a suboptimal solution is provided. Then, a second phase is executed for optimization purposes. The latter approach is shown to provide results close to optimal ones while considerably reducing computational time.

The paper is organized as follows. In Section 2, related works in the context are introduced. Section 3 provides an overview of the reference C-RAN architecture and describes the deployment problem. In Section 4, the optimization problem is formulated, while Section 5 presents the hybrid model. In Section 6, the numerical results obtained in different scenarios are presented. Finally, Section 7 concludes the paper.
