## 3 Design of the IoT-Edge-Cloud Platform
### Orchestration and Management  
Two main tasks are managed by the orchestrator: inter-node orchestration and intra-node orchestration. Inter-node orchestration handles the distribution of services across multiple geographically dispersed Edge nodes, optimizing performance and resource usage across the system. Intra-node orchestration manages the resources within each Edge node, ensuring that computational power, memory, and other resources are used efficiently to meet the specific needs of applications. The orchestrator uses declarative configuration to manage application deployment. Through YAML files and containerized services, users define the behavior, configurations, and policies for deployment, such as where to place workloads, how many instances to  
Figure 2: he architecture of the proposed IoT–Edge–Cloud platform, exemplified for two sites, structured into three primary layers: IoT-Device, Edge, and Cloud. This design enables efficient communication and scalability for connected devices. The figure illustrates flexibility in application deployment, showing that applications can operate in either the Edge or Cloud layer or even span both. Each site includes a private 5G network, represented by a dotted square, which provides 5G connectivity to the IoT-Device layer. The Edge and Network Orchestration layer manages resource allocation across both sites, which are interconnected by a direct private fiber link, supporting seamless operation and coordination between the sites. deploy, and how to monitor key performance indicators (KPIs). Moreover, a service catalog facilitates the deployment of predefined applications and services, streamlining the on-boarding process. These definitions allow the orchestrator to automate service deployment and dynamically adjust resource allocation based on current demand, improving both performance and efficiency. A key feature of this orchestrator is its AI-driven automation, which enhances orchestration by predicting system loads, optimizing resource allocation, and making intelligent deployment decisions in real time. This AI module is composed of two primary components: the Prediction Analytics Engine and the Decision Engine. Together, these components enable the orchestrator to adapt dynamically to changing conditions, improving both performance and energy efficiency while reducing operational costs. The Prediction Analytics Engine utilizes an ML-based prediction model to anticipate future system demands. By analyzing historical CPU utilization data collected from the infrastructure and applications, the model predicts future CPU usage through an ARIMA time-series prediction approach [52]. This prediction is then used by the Decision Engine to determine the necessary scaling actions in real time, such as adjusting the number of replicas or redistributing resources across nodes. This predictive capability helps ensure that the system can proactively manage resource demands, especially during periods of high traffic or anticipated load surges. The Decision Engine evaluates real-time traffic and system conditions to determine the optimal resource allocation strategy based on one of two focus areas: performance or energy efficiency. Regarding performance optimization, the focus is on maximizing performance. The Decision Engine dynamically scales the resources in response to increased user traffic. This approach maintains the required Quality-of-Service (QoS) metrics, such as response time and throughput, even under heavy-load conditions. For example, if traffic spikes due to a sudden influx of IoT data or requests for Digital Twin applications, the Decision Engine will increase the number of application pods in real time to handle the load, thereby ensuring that response times remain low, and throughput remains high. This real-time scaling based on AI-driven predictions allows the orchestrator to meet stringent performance requirements consistently, adapting to fluctuating demand without manual intervention.