Abstract
Artificial Intelligence (AI) and edge computing have recently emerged as major trends in the ICT industry. Enterprise applications increasingly make intensive use of AI technologies and are often based on multiple components running across a computing continuum. However, the heterogeneity of the technologies and software development solutions in use are evolving quickly and are still a challenge for researchers and practitioners. Indeed, lack of solutions tailored for AI applications is observed in the areas of applications placement and design space exploration with performance guarantees, both under-developed. The aim of the AI-SPRINT “Artificial Intelligence in Secure PRIvacy-preserving computing coNTinuum” project is to develop a framework composed of design and runtime management tools to seamlessly design, partition and operate Artificial Intelligence (AI) applications among the current plethora of cloud-based solutions and AI-based sensor devices (i.e., devices with intelligence and data processing capabilities), providing resource efficiency, performance, data privacy, and security guarantees. AI-SPRINT is intended to accelerate the development of AI applications, whose components are spread across the edge-cloud computing continuum, while allowing trading-off application performance and AI models accuracy. This is accomplished by the thorough suite of design tools provided by the AI-SPRINT framework, which exposes a set of programming abstractions with the goal of hiding as much as possible the computing continuum complexity, while further providing a simple interface to define desired constraints upon which the application design is guided.
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Notes
- 1.
This work is funded by EU Grant no. 101016577. The AI-SPRINT website is accessible at https://ai-sprint-project.eu.
- 2.
Source code available at https://github.com/grycap/im.
- 3.
Source code available at https://github.com/ai-sprint-eu-project/OSCAR-P.
- 4.
Source code available at https://github.com/ai-sprint-eu-project/space4ai-d.
- 5.
Source code is available at https://github.com/ai-sprint-eu-project/AI-SPRINT-STUDIO.
- 6.
Examples applications are provided at https://gitlab.polimi.it/ai-sprint/ai-sprint-examples.
- 7.
OSCAR is available at http://github.com/grycap/oscar.
- 8.
SCAR is available at http://github.com/grycap/scar.
- 9.
Source code available at https://github.com/ai-sprint-eu-project/monitoring-subsystem.
- 10.
Source code is available at https://github.com/grycap/im.
- 11.
Source code is available at https://gitlab.polimi.it/ai-sprint/a-mllibrary.
- 12.
- 13.
- 14.
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Lattari, F., Matteucci, M., Ardagna, D. (2024). AI-SPRINT: Design and Runtime Framework for Accelerating the Development of AI Applications in the Computing Continuum. In: Barolli, L. (eds) Advanced Information Networking and Applications. AINA 2024. Lecture Notes on Data Engineering and Communications Technologies, vol 203. Springer, Cham. https://doi.org/10.1007/978-3-031-57931-8_17
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