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架构，未来的极端数据密集型计算系统正面临前所未有的设计和控制挑战，以满足对信息处理的持续和不断增长的需求。可扩展性，鲁棒性，连续可用性和服务质量是确保当今设计的系统能够在未来极端规模上以相同效率运行的关键属性。本提案中描述的研究的目的是开发理论基础和实践控制算法，以实现可扩展的设计和有效管理未来的极端数据密集型计算。具体而言，研究人员将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。这里的担忧是要了解受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）在操作员放置，数据存储，负载脱落和资源分配方面制定了分布式控制策略，以便启用有效的内部工作信息信息处理。该项目将对实现未来数据密集型信息服务系统所需的基本设计原理和控制策略产生更深入的定量理解。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。