Nez: A design-driven skeleton model for building continuum AI-based and analytic systems

Abstract

Context:

Organizations increasingly rely on artificial intelligence (AI) and machine learning (ML) to process data, automate tasks, and enhance decision-making. At the same time, the computing continuum enables AI and ML to be deployed closer to data sources, thereby reducing system latency and response time.

Objective:

Managing applications across this distributed environment is challenging due to the need for manual deployment, integration, and compliance with non-functional requirements (NFRs) such as security and fault tolerance. Therefore, there is a need for frameworks that automate the deployment and execution of computing continuum systems while integrating both functional and non-functional requirements.

Method:

This paper presents Nez, a design-driven skeleton model for building continuum AI and analytics systems. Nez construction model automatically and transparently integrates AI/ML applications with non-functional components to create continuum systems that are deployed dynamically across multiple distributed infrastructures.

Results:

We conducted case studies on the processing of medical imagery and satellite imagery to provide automatic and continuous support for decision-makers. Nez has already been deployed at the Mexican hospital, Instituto Nacional de Rehabilitación Luis Gerardo Ibarra Ibarra, to create an AI-based data flow supporting bone cancer diagnosis. The evaluation shows that Nez outperforms state-of-the-art tools such as Nextflow, Makeflow, and Parsl, achieving improvements in response time of 28.46%, 17.46%, and 23.54%, respectively.

Conclusion:

Nez efficiently transforms organizational data flow designs into continuum computing services. This enables organizations to construct continuum AI-based and analytical systems that account for both functional and non-functional requirements.