Project description
Saving data by spotting soon-to-fail storage devices
Physical infrastructure is expanding to meet the fast-growing demand for greater mobile connectivity. There are currently more than 2 billion connected computers and 30 billion smartphones, wearables and connected devices. The amount of data becomes enormous, demanding support from an ever-increasing hardware infrastructure. In such an environment, hardware failures become the norm, which may result in data losses and higher maintenance costs. The EU-funded PREFAIL project will assist in securing an innovation associate to design solutions for proactively identifying soon-to-fail storage devices, protecting users from data losses and enhancing data maintenance at the storage providers.
Objective
As the Digital Transformation of Europe, and the rest of the world, is rapidly picking up pace, the underlying physical infrastructure is similarly expanding to keep up with demand generated by over 2 billion connected computers and more than 30 billion smartphones, wearables and IoT devices. Nevertheless, Internet applications and services remain prone to inevitable hardware failures, that lead to data losses and increased maintenance costs. The primary problem lies with the cost of implementing data redundancy by constantly adding expensive hardware to cater to the needs of traditional data replication approaches (e.g. by always keeping copies of a file on multiple servers).
With the assistance of an Innovation Associate specializing in Machine Learning, Algolysis Ltd aspires to extend its cloud-based storage device monitoring service (DriveNest - www.drivenest.com) with a robust state-of-the-art failure prediction engine. Reliably identifying soon-to-fail storage devices can be a transformative capability across the ICT sector, as a range of proactive data management and mitigation services can be built on top.
Fields of science
- natural sciencescomputer and information sciencesinternetinternet of things
- natural sciencescomputer and information sciencesartificial intelligencemachine learningreinforcement learning
- engineering and technologyelectrical engineering, electronic engineering, information engineeringinformation engineeringtelecommunicationsmobile phones
Programme(s)
Funding Scheme
CSA - Coordination and support actionCoordinator
4630 LEMESOS
Cyprus
The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.