## 3 A Framework for the IoT-Edge-Cloud Continuum
### The Governance Layer  
This layer incorporates tools and practices for intelligent orchestration, monitoring, logging, auditing, and reporting (see Figure 3). It ensures adherence to proper processes, the enforcement of policies, software security, data protection, and facilitates the tracing and resolution of any issues. * **Front-end dashboard:** The front-end dashboard offers an intuitive interface for users to interact with the cognitive fog network, along with APIs that facilitate the integration of third-party applications. It is designed to be user-friendly, enabling application developers to create API calls with precise control, simplifying the complexity involved in deploying advanced services. This approach enhances the compatibility between external applications and the COGNIFOG framework, fostering an environment of interoperability. * **Multi-cluster orchestrator:*
* It is composed of two main parts that communicate with each other to exchange deployment information about the desired and the current status of the application workloads across the IoT-edge-cloud continuum:
* **Multi-cluster orchestrator manager:*
* This service is responsible for deploying the required application resources across the clusters that have been on-boarded to the COGNIFOG framework. Information related to the application description and deployment requirements will be received from two other governance layer components: the front-end dashboard and the intelligent engine. * **Multi-cluster orchestrator agent:*
* This component runs on every cluster manager that has been on-boarded to the COGNIFOG framework. It receives the application description and deployment requirements from the multi-cluster orchestrator manager and allocates those resources across its managed cloud, fog, and edge nodes. * **Intelligent engine:** The intelligent engine, also known as smart allocator, operates in tandem with the multi-cluster orchestrator, providing it with guidance on how to allocate application workloads across the available infrastructure. This decision-making process considers various factors, such as network latency, data proximity, and the availability of hardware resources, as well as their power consumption. Due to the synergies with the also ongoing Horizon Europe project CODECO [61] in terms of technical challenges and participating partners, the smart allocator conceived within the COGNIFOG framework leverages its open-source scheduling and workload migration (SWM) module [62]. SWM comprises two subcomponents: the scheduler, which handles the simultaneous placement of all pods in an application group, and the  
Figure 3: Governance layer architecture based on a multi-cluster approach (based on [60]). workload placement solver, which determines the optimal placement of application workloads on available nodes. To optimize this allocation process and enhance the overall system efficiency and performance, the smart allocator may be powered by an AI algorithm. Although fully functional AI modules are not yet available in the COGNIFOG framework, several reinforcement learning (RL) techniques are currently under study given RL's effectiveness in solving resource allocation problems within the IoT-edge-cloud continuum [63]. In the COGNIFOG context, RL agents would be trained using data collected from monitoring agents, leveraging insights derived from the nodes' operational dynamics and energy utilization patterns. Once trained, the validated RL agents would be integrated into the smart allocator, which would then be able to provide RL-based placement plans to the orchestrator. When run iteratively, this process would refine the system's configuration to achieve an optimal solution that balances multiple criteria effectively. * **Policy manager:** The policy manager oversees the compliance of SLA policies of deployed applications before deployment and at runtime. It constantly analyzes feedback received from the monitoring server against application policies.