DC:Small: "Synergizing statistical machine learning and stochastic system modeling with application to real systems".

DC：Small：“将统计机器学习和随机系统建模与实际系统的应用相结合”。

• 批准号: 0917340
• 负责人: Leana Golubchik
• 金额: $ 15.7万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-15 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0917340&HistoricalAwards=false
• 关键词: DC; Small; Synergizing; statistical; machine

The scale and complexity of highly distributed data intensive systems is approaching a point where traditional performance evaluation techniques are becoming difficult to apply. Specifically, use of traditional stochastic performance evaluation methods encounters difficulties in (1) complexity (i.e., scale of the models and intractability of corresponding solution techniques) and (2) parameter estimation (i.e., needed by the models).In this project we seek to address these two challenges through the use of machine learning techniques. Such techniques have not been traditionally employed in this area, but have emerged recently as a possible direction. We envision that this will lead us not only to better machine learning approaches but will also facilitate merging of machine learning-based techniques with more traditional approaches to performance evaluation, where we anticipate obtaining better results than can be obtained through either approach alone.The broader impacts of this work will be to enable a deeper understanding of the role, advantages, and limitations of machine learning approaches in performance evaluation of large-scale systems as well as their relationship with more traditional approaches. Broader impact also includes improved interdisciplinary education at the graduate and undergraduate levels and diversity efforts.

高度分布的数据密集型系统的规模和复杂性正在接近一个点，传统的性能评估技术变得难以应用。 具体地，使用传统的随机性能评估方法遇到以下困难：（1）复杂性（即，模型的规模和相应解决方案技术的难处理性）和（2）参数估计（即，在这个项目中，我们试图通过使用机器学习技术来解决这两个挑战。 这种技术在这一领域传统上没有采用，但最近作为一个可能的方向出现。 我们预计，这不仅将引导我们找到更好的机器学习方法，而且还将促进基于机器学习的技术与更传统的性能评估方法的融合，我们预计会获得比单独使用任何一种方法更好的结果。这项工作的更广泛影响将是使我们能够更深入地了解机器学习的作用、优势机器学习方法在大规模系统性能评估中的局限性以及它们与传统方法的关系。 更广泛的影响还包括改善研究生和本科生的跨学科教育以及多样性努力。