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

高度分布式数据密集型系统的规模和复杂性正在接近传统性能评估技术变得难以应用的地步。具体地说，使用传统的随机性能评估方法在(1)复杂性(即模型的规模和相应的求解技术的难解性)和(2)参数估计(即模型所需的)方面遇到困难。在本项目中，我们试图通过使用机器学习技术来解决这两个挑战。这种技术传统上并没有在这一领域中使用，但最近出现了一个可能的方向。我们预计，这不仅将导致更好的机器学习方法，而且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，我们预计将获得比仅通过这两种方法获得的更好的结果。这项工作的更广泛的影响将使我们能够更深入地理解机器学习方法在大规模系统性能评估中的作用、优势和局限性，以及它们与更传统方法的关系。更广泛的影响还包括改善研究生和本科生的跨学科教育和多样化努力。

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.