CSR:Medium:Collaborative Research: Stochastically Robust Resource Allocation for Computing

随机稳健的计算资源分配该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。并行、分布式和基于internet的计算、通信和信息系统是机器和网络的异质混合。由于不确定因素，例如意外的机器故障、系统工作负载的变化或对系统参数的不准确估计，它们经常经历性能下降。系统性能对不确定性的鲁棒性是很重要的。计算机系统健壮意味着什么？如何描述健壮性？一个人如何判断健壮性的说法是否正确，或者系统是否会失败？如何决定两个系统中哪一个更健壮？这些都是我们在这个项目中要解决的问题，我们的团队包括来自科罗拉多州立大学和科罗拉多大学的教师、研究生和本科生，以及来自工业界（DigitalGlobe）和国家实验室（NCAR，国家大气研究中心）的同事。我们正在设计模型、度量、数学和算法工具，以及策略，用于(1)导出健壮的系统资源管理方案，以及(2)在给定不确定性的情况下量化满足性能需求的概率。我们正在通过与DigitalGlobe和NCAR的合作来验证我们的研究，DigitalGlobe为谷歌Maps和微软虚拟地球提供图像，NCAR的研究活动包括对严重和灾难性天气的预测。正在发展的健壮概念具有广泛的适用性，并将大大有助于满足国家建立和维持健壮的信息技术基础设施的需要。

Stochastically Robust Resource Allocation for ComputingThis award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).Parallel, distributed, and Internet-based computing, communication, and information systems are heterogeneous mixtures of machines and networks. They frequently experience degraded performance due to uncertainties, such as unexpected machine failures, changes in system workload, or inaccurate estimates of system parameters. It is important for system performance to be robust against uncertainties.What does it mean for a computer system to be ?robust?? How can robustness be described? How does one determine if a claim of robustness is true, or if a system will fail? How can one decide which of two systems is more robust? These are the types of issues we address in this project, with our team of faculty, graduate students, and undergraduate students from Colorado State University and the University of Colorado, and colleagues in industry (DigitalGlobe) and a national laboratory (NCAR, National Center for Atmospheric Research).We are designing models, metrics, mathematical and algorithmic tools, and strategies for (1) deriving system resource management schemes that are robust, and (2) quantifying the probability of meeting performance requirements given uncertainties. We are validating our research by working with DigitalGlobe, which supplies images to Google Maps and Microsoft Virtual Earth, and NCAR, whose research activities include the prediction of severe and catastrophic weather. The robustness concepts being developed have broad applicability, and will significantly contribute to meeting national needs to build and maintain robust information technology infrastructures.