EAGER-Dynamic Data: A New Scalable Paradigm for Optimal Resource Allocation in Dynamic Data Systems via Multi-Scale and Multi-Fidelity Simulation and Optimization
EAGER-动态数据:通过多尺度和多保真度仿真和优化实现动态数据系统中最佳资源分配的新可扩展范式
基本信息
- 批准号:1462409
- 负责人:
- 金额:$ 24.94万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2015
- 资助国家:美国
- 起止时间:2015-09-01 至 2018-08-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
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.
在工程和自然系统中,泛在传感和控制的基本目标是了解、分析和优化这些系统的运行条件。虽然经典的反馈控制理论为满足基于评估的系统状态的运行目标和约束奠定了坚实的基础,但传统的控制范式对现代动态大数据和复杂系统的适用性有限。根本的挑战是如何处理、融合和计算来自多个异类和分布式来源的数据,以便及时做出最优决策。该项目将开发创新办法,使之能够无缝和有效地整合多实体、多式联运和多保真数据收集活动的前所未有的动态互动,以及计算不同级别和规模的系统运行条件。技术的发展将使系统中的多个实体能够以高效和稳健的方式评估非常大的决策空间,并向上扩展到无处不在的传感带来的大数据环境。该研究成果有可能显著提高动态数据系统、模拟和优化研究的最新水平,潜在地开辟了一条改进各种应用系统执行的新途径。尽管这项研究是通用的,也适用于其他动态数据工程系统,但它的具体动机是半导体行业的大数据问题,半导体行业是美国和世界经济的关键部门。研究成果将通过技术出版物和专题介绍以及课堂教学进行传播,GMU将在那里开发一门新的多学科课程,并向广泛的学生提供。这项探索性研究的目标是开发一种新的可扩展计算范式,用于动态数据系统中的实时最优资源分配。这项研究的变革性方面是认识到,动态数据系统的成功执行依赖于实时的全球态势感知和及时将感知转化为(接近)最优资源分配决策的能力。根本的技术突破是一个新的多尺度和多保真度模拟和优化框架,它以自适应、高效和稳健的方式集成了多尺度和多保真度级别的数据收集和决策。多尺度模拟和优化允许使用本地化数据确定有前景的局部规模资源分配决策。然后通过全局规模的多保真模拟和优化来评估局部规模的资源分配决策,以寻求系统范围内的最优决策。这种集成的多尺度和多保真范例利用了局部尺度的响应性和全球尺度的全局态势感知,从而具有在实时决策过程中获得效率和稳健性的潜力。通过多模式和多保真数据的高效和可扩展融合,系统中的分布式实体监控动态数据系统的操作条件,并响应于系统操作条件中的扰动自主控制仪器和数据收集过程。利用信息的价值,决策模型能够对数据收集、计算和资源分配决策进行联合评估,以动态地调度将对动态数据系统的成功执行做出最大贡献的任务并确定优先级。
项目成果
期刊论文数量(0)
专著数量(0)
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会议论文数量(0)
专利数量(0)
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Jie Xu其他文献
Design Challenges and Guidelines for Persuasive Technologies that Facilitate Healthy Lifestyles.
促进健康生活方式的说服性技术的设计挑战和指南。
- DOI:
- 发表时间:
2012 - 期刊:
- 影响因子:0
- 作者:
Jie Xu;Ping;Scott Uglow;Alison Scott;E. Montague - 通讯作者:
E. Montague
On demand generation of drop and bubble in a microfluidic chip
微流控芯片中按需生成液滴和气泡
- DOI:
- 发表时间:
2008 - 期刊:
- 影响因子:0
- 作者:
Jie Xu;Daniel Attinger - 通讯作者:
Daniel Attinger
Structure and Properties of Ultrathin SiOx Films on Cu(111)
Cu(111)上超薄SiOx薄膜的结构与性能
- DOI:
10.1021/acs.langmuir.2c01701 - 发表时间:
2022 - 期刊:
- 影响因子:3.9
- 作者:
Jie Xu;Changle Mu;Mingshu Chen - 通讯作者:
Mingshu Chen
Designing messages with high sensation value: When activation meets reactance
设计具有高感觉价值的消息:当激活遇到抗拒时
- DOI:
10.1080/08870446.2014.977280 - 发表时间:
2015 - 期刊:
- 影响因子:3.3
- 作者:
Jie Xu - 通讯作者:
Jie Xu
Well dispersive Ni nanoparticles embedded in core-shell supports as efficient catalysts for 4-nitrophenol reduction
嵌入核壳载体中的分散性良好的镍纳米粒子作为 4-硝基苯酚还原的有效催化剂
- DOI:
10.1007/s11051-019-4551-0 - 发表时间:
2019-06 - 期刊:
- 影响因子:2.5
- 作者:
Xiaoshan Yang;Zhenwei Wang;Yuanyuan Shang;Yingjiu Zhang;Qing Lou;Baojun Li;Jie Xu - 通讯作者:
Jie Xu
Jie Xu的其他文献
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{{ truncateString('Jie Xu', 18)}}的其他基金
Elucidating Mechanisms of Metal Sulfide-Enabled Growth of Anoxygenic Photosynthetic Bacteria Using Transcriptomic, Aqueous/Surface Chemical, and Electron Microscopic Tools
使用转录组、水/表面化学和电子显微镜工具阐明金属硫化物促进不产氧光合细菌生长的机制
- 批准号:
2311021 - 财政年份:2023
- 资助金额:
$ 24.94万 - 项目类别:
Standard Grant
Collaborative Research: CCSS: Hierarchical Federated Learning over Highly-Dense and Overlapping NextG Wireless Deployments: Orchestrating Resources for Performance
协作研究:CCSS:高密度和重叠的 NextG 无线部署的分层联合学习:编排资源以提高性能
- 批准号:
2319780 - 财政年份:2023
- 资助金额:
$ 24.94万 - 项目类别:
Standard Grant
SAI-R: Strengthening American Electricity Infrastructure for an Electric Vehicle Future: An Energy Justice Approach
SAI-R:加强美国电力基础设施以实现电动汽车的未来:能源正义方法
- 批准号:
2228603 - 财政年份:2022
- 资助金额:
$ 24.94万 - 项目类别:
Standard Grant
CAREER: Wireless InferNets: Enabling Collaborative Machine Learning Inference on the Network Path
职业:无线推理网:在网络路径上实现协作机器学习推理
- 批准号:
2044991 - 财政年份:2021
- 资助金额:
$ 24.94万 - 项目类别:
Continuing Grant
Collaborative Research: SWIFT: SMALL: Understanding and Combating Adversarial Spectrum Learning towards Spectrum-Efficient Wireless Networking
合作研究:SWIFT:SMALL:理解和对抗对抗性频谱学习以实现频谱高效的无线网络
- 批准号:
2029858 - 财政年份:2020
- 资助金额:
$ 24.94万 - 项目类别:
Standard Grant
CCSS: Collaborative Research: Towards a Resource Rationing Framework for Wireless Federated Learning
CCSS:协作研究:无线联邦学习的资源配给框架
- 批准号:
2033681 - 财政年份:2020
- 资助金额:
$ 24.94万 - 项目类别:
Standard Grant
Collaborative Research: CNS Core: Small: Towards Automated and QoE-driven Machine Learning Model Selection for Edge Inference
合作研究:CNS 核心:小型:面向边缘推理的自动化和 QoE 驱动的机器学习模型选择
- 批准号:
2006630 - 财政年份:2020
- 资助金额:
$ 24.94万 - 项目类别:
Standard Grant
Collaborative Research: Improving Power Grids Weather Resilience through Model-free Dimension Reduction and Stochastic Search for Optimal Hardening
合作研究:通过无模型降维和随机搜索优化强化来提高电网的耐候能力
- 批准号:
1923145 - 财政年份:2019
- 资助金额:
$ 24.94万 - 项目类别:
Standard Grant
Collaborative Research: Towards High-Throughput Label-Free Circulating Tumor Cell Separation using 3D Deterministic Dielectrophoresis (D-Cubed)
合作研究:利用 3D 确定性介电泳 (D-Cubed) 实现高通量无标记循环肿瘤细胞分离
- 批准号:
1917295 - 财政年份:2019
- 资助金额:
$ 24.94万 - 项目类别:
Standard Grant
Collaborative Research: NSF/ENG/ECCS-BSF: Complex liquid droplet structures as new optical and optomechanical materials
合作研究:NSF/ENG/ECCS-BSF:复杂液滴结构作为新型光学和光机械材料
- 批准号:
1711798 - 财政年份:2017
- 资助金额:
$ 24.94万 - 项目类别:
Standard Grant
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