EAGER-Dynamic Data: A New Scalable Paradigm for Optimal Resource Allocation in Dynamic Data Systems via Multi-Scale and Multi-Fidelity Simulation and Optimization

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The fundamental objective of ubiquitous sensing and control in engineered and natural systems is to understand, analyze, and optimize operational conditions of these systems. Although the classical feedback control theories lay a solid foundation to enable meeting operating goals and constraints based on the assessed system states, the traditional control paradigm has limited applicability to the modern dynamic big data and complex systems. The fundamental challenge is the efficiency of processing, fusing, and computing of data from multiple heterogeneous and distributed sources to arrive at a timely and optimal decision. This project will develop innovative approaches to enable seamlessly and efficiently integrating unprecedented dynamic interactions of multiple entities multimodal and multi-fidelity data collection activities, and the computing of systems operational conditions at different levels and scales. The technological developments will enable the evaluation of a very large decision space for multiple entities in the system in an efficient and robust manner, and scale up to the big data environment brought forth by ubiquitous sensing. The research outcome has a potential to significantly advance the state of the art in dynamic data system, simulation, and optimization research, potentially opening a new avenue to improve the execution of a large variety of application systems. The research, while generic and applicable to other dynamic data engineered systems, is specifically motivated by the big data problem in semiconductor industry, a crucial sector of the US and world economy. Research findings will be disseminated through technical publications and presentations as well as classroom teaching where a new multi-disciplinary course will be developed at GMU and offered to a wide-range of students. The objective of this exploratory research is to develop a new scalable computational paradigm for real-time optimal resource allocation in dynamic data systems. The transformative aspect of this research is the recognition that the successful execution of a dynamic data system relies on real-time global situational awareness and the capability to translate awareness into (near) optimal resource allocation decisions in a timely manner. The fundamental technical breakthrough is a new multi-scale and multi-fidelity simulation and optimization framework that integrates data collection and decision making at multiple scales and multiple fidelity levels in an adaptive, efficient, and robust manner. Multi-scale simulation and optimization allows identifying promising local scale resource allocation decision using localized data. Local scale resource allocation decisions are then evaluated by global scale multi-fidelity simulation and optimization in search of the optimal system-wide decision. Such an integrated multi-scale and multi-fidelity paradigm exploits the responsiveness at local scale and the global situational awareness at the global scale, and thus has the potential to attain both efficiency and robustness in the real-time decision making process. Through efficient and scalable fusion of multi-modal and multi-fidelity data, distributed entities in the system monitor the operating conditions of the dynamic data system and autonomously control the instrumentation and data collection process in response to perturbations in system operating conditions. With value of information, the decision model enables joint evaluation of data collection, computing, and resource allocation decisions to dynamically schedule and prioritize tasks that would contribute most to the successful execution of dynamic data systems.