Quality of Service (QoS) and Quality of Experience (QoE) are the most relevant requirements for new advanced services comprising integrated cooperation between processing, storage, sensing, and transmission capabilities. As reliance on technology grows, businesses increasingly turn to Wide Area Networks (WANs) connectivity solutions to ensure reliable and efficient communication between their locations and cloud data centers. With new applications with stringent requirements, QoS and QoE have become critical priorities for companies looking to remain competitive and provide a seamless customer experience. As a result, many organizations require reliable and high-performing WANs to effectively transmit critical data between their branches and cloud data centers. We examine Multi-Protocol Label Switching (MPLS technology), which is the historical choice for WANs but has significant disadvantages in cost and performance. Then, we analyze the novel Software-Defined Wide Area Network (SD-WAN) technology that allows for real-time flexible, dynamic reconfiguration of network devices to meet network measurements and service requirements. Furthermore, we introduce a first work of my Ph. D. that examines the use of a Reinforcement Learning algorithm in a new SD-WAN topology scenario in which we consider both direct WAN connections between Customer Premises Equipment (CPEs) located within enterprise premises and CPEs used as peering points for traffic routing. This paper presents our initial analysis of these technologies and the main ideas guiding me in my Ph. D. program.