## 6 Discussion and Future Directions  
As cities continue to grow and face increasingly complex challenges, the Cloud-Edge-IoT continuum presents a promising approach for developing more responsive, efficient, and sustainable urban systems. By leveraging the strengths of cloud computing, edge computing, and IoT technologies, this continuum has the potential to transform urban management and improve the quality of life for city dwellers. However, realising this potential will require continued research and innovation to address the technical, organisational, and societal challenges associated with implementing such complex, distributed systems in urban environments. The COGNIFOG project has been addressing some of these open issues by developing a CEI continuum platform that allows us to explore real-world deployments of Smart City applications and services. In this paper, we have reported on our initial architecture and the use of a technology demonstrator to validate some of our initial design decisions. In particular, we have developed Quick Start, a framework for easy installation and use of COGNIFOG. We have demonstrated its use through a technology demonstrator that deploys a typical Smart City application across the CEI continuum, and we have shown how COGNIFOG can be used to monitor the performance of the application across the continuum, and dynamically reconfigure when application constraints are not met. This paper and the initial experiments help validate our overall approach. By characterising Smart City applications, and identifying their requirements, we have been able to direct the development of the COGNIFOG architecture to meet those requirements. The experimental data reported in this paper both validates the architecture and helps us shape the next steps in the project. However, while we believe that the work reported here is a valuable initial analysis of the role of the CEI continuum in supporting Smart City services, we recognize that COGNIFOG is in early development and will require continued improvement to meet the needs of cities. As a project, we plan to address several COGNIFOG-specific issues in the near future. These include:  
* Multi-Cluster Orchestration: In the Quick Start implementation of COGNIFOG, orchestration is primarily carried out within clusters. While this is sufficient for many small-scale applications, multi-cluster orchestration is needed for our large-scale trials, especially when involving geographically distributed and heterogeneous clusters across on-premises, hybrid, and even multi-cloud environments. COGNIFOG has been architected with such support [3] and this will be integrated in preparation for our three real-world trials. * Enhancing orchestration intelligence: As indicated in Section 5.3, integrating AI-driven mechanisms to optimize resource allocation and workload distribution across the IoT-edge-cloud continuum is under development. By better exploiting Reinforcement Learning algorithms we aim to develop better orchestration for our three trials. Of particular concern is Energy Management. The current COGNIFOG platform has basic support for energy optimisations using simple device monitoring. We plan to extend this with better support for consumption-driven placement decisions and reduced networking costs. * Improved usability for developers: Our current system uses a front-end dashboard to allow developers to model their application deployments and express constraints. We aim to improve this with more flexibility and better PolyGraph integration to allow developers to better understand trade-offs available in the continuum. In addition to these short-term, concrete steps to improve COGNIFOG, there are still several significant open research areas in the CEI continuum which we plan to address in the future using COGNIFOG. These include:  
* Cognitive Self-Management and Intent-Based Systems: Container-based orchestration has emerged as the de facto standard to efficiently manage resources across the continuum.