IDIEA-DC: An Infrastructure for Distributed Intelligence Experimentation and Architectures in the Digital Continuum: from IoTs to the Cloud

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This project sets the stage for leveraging the concurrent and synergistic advances in digital domains such as machine learning, high-performance computing, big data, clouds, and smart and connected devices to create new breads of applications that were hardly possible to conceive and deploy before. This can be productively accomplished by presenting this plethora of different domains to application developers as one integrated system, which is referred to here as the digital continuum. This project creates a testbed infrastructure for such digital continuum with capabilities for application deployments, task distributions, performance monitoring and optimizations, and data sets for experimentation with emphasis on distributed intelligence. The concepts and access to such testbed are shared with the community in order to open new doors for innovative system and application research in the digital continuum as a system. Convergence research is identified as one of the big ideas for future progress and investments. The digital continuum is an example of such converged systems. This project creates an experimental infrastructure, IDIEA-DC that can mimic the digital continuum from the edge devices to the data center. IDEA-DC also provides productive interfaces for exploring and testing research ideas in support of launching distributed machine learning applications on the digital continuum as a system, such as federated learning. It supports monitoring and control for intelligent and dynamic cross-layer optimizations with respect to many different parameters including performance and bandwidth availability, energy and quality of service. Two instances of such infrastructure will be created. One will be a heavily instrumented cluster devoted to emulate such digital continuum while providing the opportunity for more accurate measurements and closer control. The second will be a cloud-based deployment. The infrastructure will be augmented with relevant data sets, as well as libraries for measurements and control.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.