Project deliverable Open Access

D1.2 - Requirements Analysis

Enrico Abate-Daga

Project member(s)
Andrei Popa; Michal Klosinski; Davide Cirillo; Daniele Lezzi; Patrick Thiem; Danilo Ardagna; Matteo Matteucci; André Martin; German Moltó; Grzegorz Timoszuk

D1.2 summarizes the initial requirements analysis of the AI-SPRINT project. The outcome of this analysis is a list of requirements for all tools which will be evolved or developed within the project. This analysis will guide all the AI-SPRINT technical activities.
The aim of AI-SPRINT (Artificial Intelligence in Secure PRIvacy-preserving computing coNTinuum) is to define a novel framework for developing and operating AI applications, together with their data, exploiting computing continuum environments, which include resources from the edge up to the cloud. AI-SPRINT will offer novel tools for AI applications development, secure execution, easy deployment, as well as runtime management and optimization. AI-SPRINT tools will allow trading-off application performance (in terms of end-to-end latency or throughput), energy efficiency, and AI models accuracy while providing security and privacy guarantees. AI-SPRINT framework will support AI applications data protection, architecture enhancement, agile delivery, runtime optimization, and continuous adaptation.
A critical success factor in the development of high quality, state of the art, software frameworks is a deep understanding of the requirements of users of the framework (e.g. developers, AI experts, service providers etc.) and of end-users of the application and services created using the framework (e.g. farmers, stroke patience, maintenance engineers etc.). The elicitation of the AI-SPRINT requirements started with the description of Personas, i.e. fictional images of typical AI-SPRINT users with their specific interests, 
experiences and demands. User Stories and Requirements were then formulated by looking at the AI-SPRINT from the perspective of the selected Personas. The providers of the three real-life Use Cases wrote the first draft of the requirements, the tool providers reviewed and enhanced these requirements from a more technical point of view, also keeping in mind current state of the art solutions (see deliverable D1.1).
The requirements analysis helped to outline the importance of some critical AI-SPRINT features. The ability to develop cloud/edge agnostic applications and to define at runtime which component will run on the edge, on the cloud or on a hybrid-cloud setup will be a pivotal strength of the tools. The excellent security features of the framework will allow its usage also in security and privacy critical domains (e.g., Personalized Healthcare). The federated training of AI models will also prove to be extremely important in areas in which data cannot be easily shared among the different parties involved in the project.
The requirements analysis also showed that the proposed reference architecture (see deliverable D1.3) can be used for all three Use Cases, with minor specific modifications. The proposed project assets (SCAR, PyCOMPs, Krake, SCONE etc.) present a very good starting point for the development of the framework; the tools need to be adapted and especially integrated to form a coherent, easy to use framework. 
It must be mentioned that the requirements analysis process is intended to be a continuous process, hence this report does not coincide with the end of requirement analysis. A requirement repository was defined and will be continuously updated until month 24.

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