## 3 SDN-Enabled Edge Cloud Architecture  
The edge cloud computing or multi-access edge cloud computing is a concept that involves placing compute and storage resources near to the user. This approach aims to meet the extensive demand of computing needs of emerging low-latency and data-intensive applications. It allows data-intensive complex applications to run closer to IoT devices, reducing delays related to the offloading of large data to the centralized cloud. To realize the distributed edge cloud computing architecture, networking plays the critical role. Therefore, cloudification of computing along with the networking provides an integrated vision across the fields of computing and networking [23,24]. A layered architecture view of SDN-enabled edge cloud computing and networking is depicted in Figure 2.  
Figure 2: Layered architecture of multi-access edge cloud network architecture. Multi-tenant user applications on an IoT/access device use edge cloud infrastructure through access networks. The IoT device layer could have millions of embedded systems, sensors, cameras, smart phones, drones, connected vehicles, smart houses, smart factory, robots, etc. These devices collect large-scale (MBs-PBs) data from a lot of multi-tenant applications and transfer those data to edge cloud clusters through LTE/5G/Wi-Fi/wire as multi-access networks. The access network provides high bandwidth, low latency, and a reliable link between access device(s) and the edge cloud layer. The edge cloud layer provides required computing, storage, and networking services to the user application(s). Edge clouds are distributed in nature so information or data will be processed near to the physical locations where users are connected. As a result, the latency is also reduced compared to the core cloud. The total end-to-end latency of an application depends on various parameters such as processing delay, propagation delay, transmission delay, routing delay, and switching delay (in an SDN-enabled network). For cloud only models, the total latency can be expressed as shown in Equation (1). \[L_{CC}=\alpha\,*\,d_{min}(ED,\,\,AP)+\beta\,*\,d(AP,CC)+t_{node} \tag{1}\]  
In Equation (1), \(L_{CC}\) is the total cloud latency, \(d_{min}(ED,\,\,AP)\) is the distance between end device to nearest access point (\(AP\)), \(d(AP,CC)\) is the distance between access point and the central cloud, \(\alpha\) and \(\beta\) are the proportionality constants, and \(t_{node}\) represents the total time a task spends at the central cloud. For the edge cloud network, the total latency can be represented as shown in Equation (2). \[L_{EC}=\alpha\,*\,d_{min}(ED,\,EC)+t_{node}+d_{s} \tag{2}\]  
In Equation (2), \(L_{EC}\) is the total edge cloud latency, \(d_{min}(ED,\,EC)\) is the distance between end device and edge cloud, and \(d_{s}\) is the control plane switching latency [8]. The edge cloud layer can have two sublayers: infrastructure and virtualization and software defined network (SDN). Virtualization and SDN are the two key enabling technologies to virtualize resources and manage network, computing, and storage devices. We will discuss these two sublayers in detail later in Sections 3 and 4. A lot of new services can be deployed at the edge sites which are close to the users or customers. This reduces the burden on the centralized cloud server and the congestion in the core networks. Thousands of IP backhaul links are deployed on the core network. Through these IP backhaul links, edge cloud clusters will be connected to the central cloud. There are several core network technologies such as IP networks, IP/MPLS, Optical networks, Satellites, cellular, SD-WAN, etc. Edge cloud to central cloud bandwidth might be limited with high latency; in some cases, links are unreliable which occurs in rural areas. Most of the data will be processed at the edge, so it will reduce the pressure on the remote center cloud.