In the current state of distributed computing, enabling secure and efficient data storage and processing at the fog and edge layer is sub-optimal. Facilitating the extraction of high-level analytic insights by introducing novel decentralized algorithms and open APIs is also an issue that RAINBOW aspires to address.

New modelling techniques and mechanisms are needed to compose and coordinate resources across heterogeneous cloud platforms, including micro local clouds, private enterprise clouds, aggregated and hybrid cloud models. Techniques that guarantee privacy, security, identity are essential and quite frankly, the available cloud computing platforms have not yet caught up with the rising demand. RAINBOW aims to address the issues arising from limited memory availability, storage and computational capabilities of fog nodes that are closer to where data are generated into the cloud architecture and allow to make intelligent decisions when to move computation from the edge to the cloud, while taking into account the network capabilities as well as the security and/or sensitivity of data.

A substantial technological challenge which RAINBOW addresses is to develop competitive cloud solutions based on advanced cloud platforms and services as well as cloud-based software and data applications. Such solutions should also  address stringent security, data protection, performance, resilience and energy-efficiency requirements to respond to the future digitization needs of both the industry and the public sector.

RAINBOW’s solution will provide significant benefits for popular cloud platforms and fog middleware, by pushing intelligence to the network edge while also ensuring security and privacy primitives across the device-fog-cloud-application stack. Within this project, we will show that by bringing artificial intelligence to the “edge”, devices such as connected vehicles, drones or factory equipment are able to quickly learn and respond to their environments. A key aspect in our solution is to support multi-criteria optimization and decision-making algorithms for driving a continuously evolving deployment and configuration of cloud resources and edge devices. RAINBOW addresses the market’s increasing demand for privacy, security and identity by containing a dedicated work-package which focuses on developing the attestation modelling techniques that will enable the trusted, privacy-preserving and accountable (edge and mesh) device authentication and trust management services.

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