In a smart city environment, we look at a new, upcoming generation of vehicles running on electric power supplied by on-board batteries. Best recharging options include charging at home, as well as charging at public areas. In this setting, electric vehicles will be informed about public charging stations using wireless communications. As the charging stations are shared resources, cooperating electric vehicles have the potential to avoid unbalanced use of recharging stations and lengthy waiting times.We present a model for electric vehicles and their battery depletion, vehicle mobility, charging stations, and give a solution for optimal placement of charging stations in a smart city. Our placement approach is based on genetic programming and simulation of electric vehicles which move on a real map of a European city. We show that after a few evolution steps, an optimal solution of the charging infrastructure is derived based on mean trip times of electric vehicles.

One of the main goals of 5G networks is to support the technological and business needs of various industries (the so-called verticals), which wish to offer to their customers a wide range of services characterized by diverse performance requirements. In this context, a critical challenge lies in mapping in an automated manner the requirements of verticals into decisions concerning the network infrastructure, including VNF placement, resource assignment, and traffic routing. In this paper, we seek to make such decisions jointly, accounting for their mutual interaction, and efficiently. To this end, we formulate a queuingbased model and use it at the network orchestrator to optimally match the vertical's requirements to the available system resources. We then propose a fast and efficient solution strategy, called MaxZ, which allows us to reduce the solution complexity. Our performance evaluation, carried out accounting for multiple scenarios representative of real-world services, shows that MaxZ performs substantially better than state-ofthe-art alternatives and consistently close to the optimum. INTRODUCTION5G networks are envisioned to provide the computational, memory, and storage resources needed to run multiple third parties (referred to as vertical industries or verticals) with diverse communication and computation needs. Verticals provide network operators with the specification of the services they want to provide, e.g., the virtual network functions (VNFs) they want to use to process their data and the associated quality of service.Mobile network operators are in charge of mapping the requirements of the verticals into infrastructure management decisions. This task is part of the network orchestration, and includes making decisions concerning (i) the placement of the VNFs needed by the verticals across the infrastructure; (ii) the assignment of CPU, memory and storage resources to the VNFs; (iii) the routing of data across network nodes.These decisions interact with each other in ways that are complex and often counterintuitive. In this paper, we focus on the allocation of computational and network resources, and make such decisions jointly, accounting for (i) the requirements of each VNF and vertical; (ii) the capabilities of the network operator's infrastructure; (iii) the capacity • S. Agarwal is with IIT Guwahati, India. F. Malandrino and C.-F. Chiasserini are with Politecnico di Torino, Italy and CNR-IEIIT, Italy. S. De is with IIT Delhi, India. • A preliminary version [1] of this work was presented at the IEEE INFOCOM 2018 conference.

Thanks to network slicing, 5G networks will support a variety of services in a flexible and swift manner. In this context, we seek to make high-quality, joint optimal decisions concerning the placement of VNFs across the physical hosts for realizing the services, and the allocation of CPU resources in VNFs sharing a host. To this end, we present a queuing-based system model, accounting for all the entities involved in 5G networks. Then, we propose a fast and efficient solution strategy yielding nearoptimal decisions. We evaluate our approach in multiple scenarios that well represent real-world services, and find it to consistently outperform state-of-the-art alternatives and closely match the optimum.

We consider a system where users aboard communication-enabled vehicles are interested in downloading different contents from Internet-based servers. This scenario captures many of the infotainment services that vehicular communication is envisioned to enable, including news reporting, navigation maps and software updating, or multimedia file downloading. In this paper, we outline the performance limits of such a vehicular content downloading system by modelling the downloading process as an optimization problem, and maximizing the overall system throughput. Our approach allows us to investigate the impact of different factors, such as the roadside infrastructure deployment, the vehicle-to-vehicle relaying, and the penetration rate of the communication technology, even in presence of large instances of the problem. Results highlight the existence of two operational regimes at different penetration rates and the importance of an efficient, yet 2-hop constrained, vehicleto-vehicle relaying.

Content downloading in vehicular networks is a topic of increasing interest: services based upon it are expected to be hugely popular and investments are planned for wireless roadside infrastructure to support it. We focus on a content downloading system leveraging both infrastructure-to-vehicle and vehicle-to-vehicle communication. With the goal to maximize the system throughput, we formulate a max-flow problem that ac counts for several practical aspects, including channel contention and the data transfer paradigm. Through our study, we identify the factors that have the largest impact on the performance and derive guidelines for the design of the vehicular network and of the roadside infrastructure supporting it.