Started at: 01-01-2023
Ends on: 31-12-2025
Budget: € 5 030 875
Areas: Internet of Things and Software Networks
Next generation enablers, such as IoT, AI and cloud computing, open new opportunities to deal with world’s current and future societal, environmental, and economic challenges. However, they come with significant data management challenges. According to IDC, the total amount of data generated only by connected devices will exceed 40 trillion gigabytes by 2025. In most of the current systems, data storage and analysis happen on centralized locations on the cloud. This is pushing network capacity to its limits and it shows the growing demand for a software solution capable of monitoring and analyzing data flows, not just in the cloud, but also closer to data sources along the IoT-to-cloud path, in the fog.
COGNIFOG targets those challenges and will provide a software solution as a Cognitive Fog Framework (Cognitive-Fog) for monitoring and analyzing data flows along the IoT-edge-cloud continuum, enabling the processing of data closer to its source, and providing real-time responses for many smart IoT applications. The main project’s objectives are:
• Reduce energy consumption and latency in next generation IT systems by reducing the network traffic, by analysing data at the edge in a distributed manner, closer to where they are generated, rather than routing them through the communication networks to a data centre.
• Reduce OPEX and faster service provisioning by providing a cognitive, self-adaptive framework with minimum or no human intervention, with dynamic provisioning of computing, storage, and networking resources along the far-edge-to-edge-to-cloud path.
• Ensure European leadership by providing an open interoperable framework with open APIs for application developers to rapidly create and deploy applications benefiting the edge-cloud continuum on top of heterogenous IoT/IT systems.
COGNIFOG will validate project results in three representative application domains: critical collaboration missions, smart health, and smart industry.
• A new AI-enabled Cloud-edge framework (Cognitive Cloud) that will automatically adapt to the growing complexity and data deluge by integrating seamlessly and securely diverse computing and data environments, spanning from core cloud to edge.
• Resource management should consider the openness and trustworthiness of the underlying resource management layers. The Cognitive Cloud will interface with all the layers in the computing continuum plane and will learn through the monitoring and management of resources deployed on Cloud/Edge.
• Applying AI techniques will cater for dynamic load balancing to optimise energy efficiency and maintain balanced data traffic and high, distributed, reliable throughput from cloud to edge according to the application and user needs and the underlying infrastructures. The framework will also dynamically adapt the processing capacity of the cloud to the varying supply of green energy to optimise its environmental footprint.
• Application developers will be empowered with greater control over the network, computing and data infrastructures and services, and the end-user will benefit from seamless access to a continuous service environment.
• Improved European leadership in the global data economy.
• Maximised social and economic benefits from the wider and more effective use of data.
• Reinforced Europe’s ability to manage urgent societal challenges.
• Globally attractive, secure and dynamic data-agile economy, by developing and enabling the uptake of the next-generation computing and data technologies and infrastructures (including space infrastructure and data), enabling the European single market for data with the corresponding data spaces and a trustworthy artificial intelligence ecosystem.
Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.
COGNIFOG is funded by the European Union Framework Programme for Research and Innovation Horizon Europe under grant agreement N° 101092968