## 6 Discussion and Future Directions
Nonetheless, the complexity of the resulting architectures and the evolving demands of applications complicate manual management and underscore the necessity for automation [36]. In response, smart container orchestration explores the effectiveness of various ML-assisted learning techniques for dynamic workload allocation, predictive scaling, and anomaly detection [41]. Future research should focus on developing self-managing systems that can autonomously adapt to changing conditions based on user intents. These systems should be capable of understanding high-level goals and translating them into actionable plans and will require exploring architectures that can self-adapt and self-organise in response to dynamic environments. These architectures should be able to reconfigure themselves to optimise performance and resource utilisation. * Edge AI and Federated Learning: Integrating AI at the edge and employing federated learning techniques can enhance data privacy and reduce latency. Research should focus on developing efficient edge AI models and federated learning frameworks [53; 54]. AI-driven data management in the CEI continuum is increasingly employed, with intelligent algorithms responsible for classifying, prioritizing, and storing data in real time [55]
* Green Computing in the Cognitive Cloud-Edge-IoT Continuum: Researching green computing solutions will help minimise the environmental impact of the continuum. This includes developing energy-efficient algorithms, hardware, and practices to promote sustainability. Optimizing energy consumption across the CEI continuum is critical for minimizing carbon footprints and reducing operational costs. At the cloud level, dynamic resource allocation techniques allow data centres to scale resources up or down based on real-time demand, thus avoiding over-provisioning [56]. On the edge side, the focus shifts to lightweight and efficient hardware, such as novel low-power devices that may prioritize energy efficiency while maintaining computational capability [43]. Lastly, energy-harvesting technologies, such as solar-powered IoT sensors and backscatter communication, offer the ability to extend device lifetime in remote or resource-constrained environments [57]. Conceptualization, R.L. and T.A. ; methodology, R.L., T.A., and A.B. ; software, A.B. ; validation, A.B. ; investigation, all authors; writing--original draft preparation, R.L. ; writing--review, and editing, R.L., T.A., A.B. and S.A. All authors have read and agreed to the published version of the manuscript. This work has received funding from the European Union's Horizon Europe Research and Innovation Framework Programme under Grant Agreement No 101092968 (project COGNIFOG) and from the Swiss State Secretariat for Education, Research and Innovation (SERI)\". The raw data supporting the conclusions of this article will be made available by the corresponding author upon request. This work makes use of software developed in the COGNIFOG project. We acknowledge the contributions of our colleagues in the design and development of the COGNIFOG software platform. These include [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]. Conflicts of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.