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QUICK 2024 : Second International Workshop on Urgent Analytics for the Computing Continuum

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Link: https://quick-workshop.github.io/
 
When May 6, 2024 - May 6, 2024
Where Philadelphia, USA
Abstract Registration Due Dec 15, 2023
Submission Deadline Dec 22, 2023
Notification Due Feb 23, 2024
Final Version Due Mar 4, 2024
Categories    computing continuum   urgent computing   cloud computing   edge computing
 

Call For Papers

Call For Papers

Second International Workshop on Urgent Analytics for the Computing Continuum (QUICK'2024)
Co-located with the 24th IEEE/ACM International Symposium on Cluster, Cloud, and Internet Computing (CCGrid 2024)

----------------- Important Dates --------------------
Abstract Submission Deadline: Dec 15, 2023 AoE
Paper Submission Deadline: Dec 22, 2023 AoE
Author Notification: Feb 23, 2024
--------------------------------------------------------

Philadelphia, USA, | May 6, 2024
https://quick-workshop.github.io/

The Second Workshop on Urgent Analytics for Computing Continuum (QUICK'2024) provides a platform for distributed applications that enable important decision-making under time and quality constraints while maintaining desired confidence. We seek scientific contributions that leverage the aggregation of heterogeneous resources along the data path from the IoT/Edge to the Cloud and beyond, also known as the Computing Continuum, to support critical analytics applications. Data-driven dynamic workflows, which combine knowledge from multiple data sources and integrate it on-demand with distributed, large-scale computational models, have the potential to address today’s global grand challenges in science, engineering, and society.

We are looking for original high-quality research and position papers on urgent applications, services, and system software for the computing continuum.
Topics of interest for workshop submissions include (but are not limited to):

- Algorithms, models and systems considerations in designing urgent applications in the Computing Continuum
- Programming support for user expectations and constraints in terms of response time, solution quality, data resolution, cost, energy, etc.
- Run-time techniques to provide flexible execution models for computation and communication.
- Resource management frameworks and interfaces supporting scheduling, resource allocations and application execution for the computing continuum.
- Use of AI and ML techniques to steer urgency in systems and applications.
- Experiences and use cases applying urgent science to computing continuum infrastructures.
- Autonomic Computing in the Computing Continuum
- Feedback, Control and Observability across the Computing Continuum
- Distributed Machine Learning in the Computing Continuum
- Edge Intelligence models and architectures
- Policy driven service and resource life-cycle management

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