DC: Small: Collaborative Research: Managing Extreme-Scale Data Intensive Computing: Fundamental Design and Control Strategies

DC：小型：协作研究：管理超大规模数据密集型计算：基本设计和控制策略

• 批准号: 0916440
• 负责人: Cathy Xia
• 金额: $ 28万
• 依托单位: Ohio State University Research Foundation -DO NOT USE
• 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 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0916440&HistoricalAwards=false
• 关键词: DC; Small; Collaborative; Research; Managing; DC; Small; Collaborative; Research; Managing

With the continuous growth of data production and the ever expanding computing infrastructure, future extreme-scale data intensive computing systems are facing unprecedented design and control challenges to meet the continuous and increasing demand for information processing.Scalability, robustness, continuous availability, and service quality are the key attributes desired to ensure that the system designed today is capable of operating with the same efficiency on the extreme scale of the future.The goal of the research described in this proposal is to develop theoretical foundations and practical control algorithms that enable the scalable design and efficient management of future extreme-scale data-intensive computing. Specifically, the researchers will1) identify fundamental design principles needed to achieve scalability when developing network infrastructure and software systems in large scale. Here the concerns are to understand the performance degradation limited by various factors, including network structure, processor speeds, buffering/storage capacities, etc.2) develop distributed control strategies on operator placement, data storage, load shedding, and resource allocation so as to enable efficient in-network information processing.The project will produce a deeper and quantitative understanding on the fundamental design principles and control strategies needed to achieve scalability, robustness and quality of service for future data-intensive information service systems. These advances will occur through a collaborative effort spanning multiple disciplines ranging from performance modeling, networking, queueing, to optimization.

随着数据生产的持续增长和不断扩大的计算基础架构，未来的极端数据密集型计算系统面临着前所未有的设计和控制挑战，以满足信息处理的持续和不断增长的需求。规范性，鲁棒性，持续可用性，持续的可用性和服务质量是为了确保所构成的效率相同的效率，以确保该系统具有相同的效率。是为了开发理论基础和实践控制算法，以实现对未来极限数据密集型计算的可扩展设计和有效管理。具体而言，研究人员将为1）确定在大规模开发网络基础架构和软件系统时所需的基本设计原则。这里的关注是了解受到各种因素的限制的性能降解，包括网络结构，处理器速度，缓冲/存储能力等。2）2）制定有关操作员放置，数据存储，负载脱落和资源分配的分布式控制策略，以便启用有效的内部工作信息处理，以实现更深入的信息和定量策略性，以实现良好的质量和定量的策略性，并进行质量的策略性，并进行质量的策略性，以实现基础的策略性，以实现基础的策略性，以实现基础的策略性，以实现基础的策略性，以实现稳固性的策略性，以实现基础的策略性。数据密集型信息服务系统。这些进步将通过涵盖从性能建模，网络，排队到优化的多个学科的协作努力进行。