## 5 Key Enablers
### Software-Defined Controllers for Edge Cloud
#### 5.3.1 SDN Controller for HomeCloud  
HomeCloud is one of the proposed works in the direction of the convergence of NFV and SDNs for edge cloud. One of the objectives of this open framework is to develop efficient edge cloud orchestration and dynamic delivery of IoT applications. NFV is considered for facilitating computing, storage, and networking closer to the user and SDNs are considered for network configuration, control, and management. Figure 8 shows the architecture of the HomeCloud framework and the interaction among different functional units.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Work Area** & **Key Points** \\ \hline
[11] & Edge computing benefits from SDN & Latency, load balancing, and computation resources \\ \hline
[46] & MEC network design optimization & Latency, reliability, and resource Mobility \\ \hline
[47] & Data offloading in MEC using SDN & Centralized management, latency, and bandwidth \\ \hline
[48] & Inter-datacenter bulk transfers & Bandwidth, utilization, traffic exchanged overthe Wide-Area \\  & & Networks (WANs) and SDN controller \\ \hline
[49] & SDN-enabled fog network & Latency, load balancing, hybrid SDN routing protocol \\  & & combining the OLSR data forwarding, traffic engineering, and OpenFlow and SDN \\ \hline
[50;51] & SDN load balancing with deep learning & Long Short-Term Memory (LSTM), reinforcement learning, latency, load balancing, Multi-access Edge Computing and SDN controller \\ \hline
[52] & SDN for edge cloud resources & Node and link provisioning, latency, and bandwidth \\  & allocation & \\ \hline
[53;54] & Ensure high standard QoS and optimize resource allocation & Latency, Bandwidth, Throughput, deep reinforcement learning (DRL) \\ \hline
[55] & Secure and intelligent services in IoT & Architecture, Blockchain, and reinforcement learning \\ \hline
[56] & Edge cloud collaboration architecture & Data-driven architecture, latency, data analytics, SDNs, and cloud manufacturing. \\ \hline
[57;58] & Resource Provisioning in Edge Cloud & Machine learning, RL, load balancing, placement of application, computation offloading, and route optimization \\  & Computing & \\ \hline
[59;60] & SDN controller placement & Machine learning, deep reinforcement learning, SDNs, network partitioning, latency, load balancing, and mobile edge cloud. \\ \hline
[61] & Controller-based edge cloud & Reinforcement learning, task placement and improve system utility. \\  & computing & Federated learning (FL), Intrusion Detection, and SDN-based \\ \hline
[62] & Intrusion detection in SDN-based edge computing &
\begin{tabular}{c} Feederated learning (FL), Intrusion Detection, and SDN-based \\ edge computing. \\ \end{tabular} \\ \hline
[18;63] & IoT and edge cloud &
\begin{tabular}{c} Deep learning, adaptive resource management, traffic \\ management, cloud-to-edge computing, and SD-WAN. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Related works on edge cloud and SDNs.  
The SDN controller in the above architecture is responsible for programming, control, configuration, and monitoring overlay networks using southbound interfaces (SBIs) and communicating to the application controller, orchestration, and NFV infrastructure server using northbound interfaces (NBI). NFV infrastructure allocates and manages network resources such as storage, computation, bandwidth, etc. Orchestration converts SLA into real deployment of application with the collaboration of the SDN controller. Finally, the application controller keeps track and monitors application-level entities with the help of the SDN controller [64].