## 1 Introduction
presented a survey on end edge cloud networks, focusing on computing paradigms. They also present a comparative study on MEC, fog, cloudlet, and cloud [10]. [PERSON], [PERSON], and [PERSON] [11] surveyed the architecture of multi-tier edge \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Features** & **Cloud Computing** & **Edge Computing** \\ \hline Architecture & Centralized & Distributed \\ \hline Latency & High & Low \\ \hline Mobility & No & Yes \\ \hline Computational capacity & High & Medium to low \\ \hline Security & Less secure & More secure \\ \hline Bandwidth usages & High network bandwidth uses & Lower network bandwidth uses \\ \hline Scalability & Easy to scale & Less scalable than cloud \\ \hline Data Processing & Through Internet & Near to the source of the data \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between cloud computing and edge computing. Source: [6; 7]. computing and SDN technologies. The authors in [12] conducted a survey on multi-access edge computing for enabling IoT applications. They also discussed computation offloading, resource allocation, mobility management, and security of MEC and IoT integration. [PERSON] and [PERSON] [13] presented a survey investigating the key enabling technologies from the perspective of IoT applications delivery on edge cloud. The study presented by [PERSON] et al. [14] described some key requirements of industrial IoT from the perspective of an edge cloud approach. They also discussed key features of various IoT platforms, such as ThingsBoard, Microsoft Azure IoT, etc., along with some open challenges. A survey on collaborative deep learning in edge cloud paradigms is presented in [15]. They also identified some open issues on collaborative deep learning in edge cloud computing. The survey paper presented by [PERSON] et al. [16] discusses the IoT edge cloud (IEC) and identifies some challenges in the context of broadband wireless networks. Although the above-mentioned surveys are inspiring, none of them specifically discussed the importance and integration of SDN-enabled controllers and orchestration in edge cloud. In light of the above, in this survey, first we extensively summarize industry-wide edge cloud use cases, killer apps, and their key challenges. After that, we introduce a holistic overview of SDN-enabled edge cloud networks. Next, we present a comprehensive survey of the research proposals on the integration of SDN controllers and orchestration for edge cloud networks. Moreover, we emphasize leveraging AI and ML to improve the capabilities of SDN controllers in the edge cloud networks. Figure 1 shows the organization of the paper. It outlines the logical structure of the research into seven main sections, each further divided into detailed subsections. This approach helps create the depth and clarity of the presented study. Figure 1: Organization of the paper. The rest of the article is organized as follows. Section 2 presents some of the key use cases and deployment areas of edge cloud networks, highlighting real-world applications. Section 3 introduces the fundamental concepts of SDN-enabled edge cloud architecture. Mobile edge cloud and fixed edge cloud are also discussed in this section. Section 4 discusses the basic infrastructure of edge cloud systems. It explores storage, computing, and networking within the context of edge cloud systems. Section 5 presents an extensive review of the key enabling technologies of edge cloud systems. It also explores several proposed SDN controllers for an edge cloud environment. Section 6 discusses major challenges in realizing edge cloud, such as dynamic offloading, controller placement, Intelligent management, workload distribution, federated and interoperated service, and security. It also outlines potential research directions addressing these challenges and advance the research area. Finally, Section 7 concludes the paper.