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

该项目为利用机器学习、高性能计算、大数据、云和智能互联设备等数字领域的并行和协同进步创造了新的应用程序，这些应用程序以前几乎不可能构思和部署。这可以通过将过多的不同领域作为一个集成系统(在这里称为数字连续体)呈现给应用程序开发人员来高效地实现。该项目为这样的数字连续体创建了一个试验台基础设施，具有应用程序部署、任务分配、性能监控和优化的能力，以及用于实验的数据集，重点是分布式智能。与社区分享这种试验床的概念和途径，以便为作为一个系统的数字连续体中的创新系统和应用研究打开新的大门。趋同研究被认为是未来进步和投资的重要理念之一。数字连续体就是这种融合系统的一个例子。该项目创建了一个实验性基础设施IDIEA-DC，可以模拟从边缘设备到数据中心的数字连续体。IDEA-DC还提供了有效的接口，用于探索和测试研究思路，以支持在数字连续体上作为一个系统启动分布式机器学习应用程序，例如联合学习。它支持针对许多不同参数(包括性能和带宽可用性、能量和服务质量)进行智能和动态跨层优化的监控。将创建此类基础设施的两个实例。其中一个将是一个配备了大量仪器的集群，致力于模拟这种数字连续体，同时为更准确的测量和更紧密的控制提供机会。第二个将是基于云的部署。基础设施将增加相关的数据集，以及用于测量和控制的库。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.