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: 0916726
• 负责人: Donald Towsley
• 金额: $ 18.5万
• 依托单位: University of Massachusetts Amherst
• 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=0916726&HistoricalAwards=false
• 关键词: 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 will 1) 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展的计算基础设施，未来的极端规模的数据密集型计算系统面临着前所未有的设计和控制挑战，以满足对信息处理的持续和增长的需求。可扩展性、鲁棒性、连续可用性和服务质量是确保今天设计的系统能够在未来的极端规模上以相同的效率运行所需的关键属性。在这个建议中描述的研究的目标是开发理论基础和实用的控制算法，使未来的极端规模的数据密集型计算的可扩展的设计和有效的管理。具体来说，研究人员将1）确定在大规模开发网络基础设施和软件系统时实现可扩展性所需的基本设计原则。这里的关注点是了解性能下降受到各种因素的限制，包括网络结构，处理器速度，缓冲/存储容量等。2）开发分布式控制策略的运营商的位置，数据存储，负载削减，和资源分配，以使有效的网络信息处理。该项目将产生一个更深入和量化的基本设计原则和控制策略，需要实现未来的数据密集型信息服务系统的可扩展性，鲁棒性和服务质量的理解。这些进步将通过跨多个学科的协作努力来实现，从性能建模、网络、排队到优化。