## 5 Roles of SDN for Cloud-Fog Enabled 5G and Beyond
### SDN in Task Offloading for Cloud-Fog  
One of the fits for SDN is in connecting different sections of cloud-fog, especially in efficiently coping with computational offloading. Task offloading refers to getting assistance from other devices in a way that another device performs tasks on behalf of the local device. Task offloading can be in the form of device-to-device offloading [46], device-to-fog offloading [47], fog-to-fog offloading [48] or fog-to-cloud offloading [2]. In the research paper [48], the authors proposed a dynamic offloading mechanism among the fog nodes to address the challenge of static offloading and oblivious link selection in vehicular networks. The incitation for fog-to-fog offloading is raising the computational overhead for a single fog node. Traditionally, the selection of an offloading node based on the most available resources can fabricate various challenges (e.g., congestion, resources under-utilization). In the proposed method, the SDN controller collects the statistics, computational capabilities status, and supported services of a fog node, using the fog agent. When an offloading request is received, the service orchestrator at the controller invokes the appropriate service for the optimal offloading node and a QoS aware path installation. QoS aware path installation is materialized, using the minimum and maximum flow data rate constraints among overloaded and offloading nodes. Thus, the controller supports an overloaded fog node without turning around to static or all offloading fog nodes uncontrollably. Although related works on computation offloading mostly focus on latency and energy consumption, the topic of reliable computation offloading is commonly left in the dark. One of the very few studies in this topic is the work of [PERSON] et al. [49] for reliable computation offloading in Internet of Vehicle (IoV). The authors also considered the partial offloading, reliability-oriented task allocation and reprocessing mechanism. In the proposed solution, for better resource orchestration, the SDN controller controls the mobile vehicles as mobile edge nodes. In addition, the cluster of edge nodes and SDN are connected by the help of the access Roadside Units (RSUs) through broadband connections. The service request from vehicles or other edge nodes are sent to the access RSU, where the decisions of the SDN controller are executed. Different schemes were considered to report the result where the amount and target of offloading vary. The latency and reliability performanceof these schemes were reported. To analyze the performance of proposed solutions, the authors considered three environments, where the links and nodes reliability could be good, poor and mixed. The simulation results showed the high reliability for latency-sensitive applications in an edge computing-enabled software-defined IoV (EC-SDIoV) network. Focusing on software defined vehicle networks, [PERSON] et al. [50] proposed a framework which deployed the SDN controller in the control layer of the remote cloud. This makes the remote cloud intelligent for the computation of tasks offloaded by a vehicle toward the RSU and addresses the problem of speedy vehicle applications randomly interacting with all the RSU in the region for computational requests. Straightforwardly, the interface among the vehicle communication unit and fog nodes is not aware of any optimal path in advance. Therefore, it leads to costly computation, traffic over oblivious links and delayed feedback from the RSU integrated fog node. Vehicle and fog nodes' transmitted features are cached in the remote cloud DC and are accessible for the controller at any time for routing decisions. RSU generates a request for the optimal path installation from the SDN controller. The controller utilizes the high-performance fog nodes for the real offloaded task by classifying and streamlining the information's table.