Cloud Computing
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More AI-driven hardware in data centres to manage workloads in the cloud

To support intensive workloads that are anticipated for cloud computing, cloud providers and heavy users like research institutions are investing heavily in specialised hardware. This goes beyond standard CPUs (with AMD outselling Intel in the data centre segment recently) to include powerful accelerators such as Graphical Processing Units (GPUs), which are the workhorses for AI training and whereby Nvidia’s data centre GPU market share is 98%. Hyperscalers are developing specific hardware like AI chips to deliver high-performance, cost-efficient cloud services. This hardware evolution requires additional innovations in infrastructure to manage the immense power density.

Maturity

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More diversified access to large AI models Increase in co-evolution between hardware and AI Collaboration between humans and AI Democratisation of XR content creation using generative AI Public investments in XR continue to increase Standardisation of data space architectures for secure and trusted data sharing Growing significance of augmented data management Trusted digital recognitions are emerging Digital Trust Frameworks: from hierarchical to distributed trust Emerging dualuse of AI in cybersecurity More accessible privacy-enhancing technologies to share data Rising pressure to prepare for post-quantum cryptography Emergence of measures for secure Internet of Things The growing need for security protocols to work in the cloud Growing investments in the European cloud infrastructure & sovereignty More AI-driven hardware in data centres to manage workloads in the cloud Increasing need for heterogeneous and hybrid computing platforms Trade-offs in accuracy with approximate computing Fixed wireless access Fibre optic networks: strategic infrastructure for the knowledge economy Enhancing quantum computing robustness and potential Quantum sensing integration to other domains

Drivers

Impact

education

Education

  • Students need to gain exposure to advanced computing environments and AI-capable hardware. This can be achieved through dedicated compulsory and elective modules/ courses, as well as project work, where the utilisation of new computing paradigms is a requirement.
Research

Research

  • Thanks to the integration of advanced AI model training and cloud computing services, researchers will have faster and more feasible access to computing resources that are applicable across several disciplines.
Operations

Operations

  • At the research institute and university level, shared infrastructure serves to reduce costs. In addition, the deployment of liquid cooling systems for computational infrastructure will improve energy efficiency and help with the sustainability of these computational resources.
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