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

基本信息

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

项目摘要

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.

随着数据生产的不断增长和计算基础设施的不断扩展，未来的超大规模数据密集型计算系统面临着前所未有的设计和控制挑战，以满足不断增长的信息处理需求。可扩展性、稳健性、持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下以相同效率运行所需的关键属性。该提案中描述的研究目标是开发理论基础和实用控制算法，以实现未来超大规模数据密集型计算的可扩展设计和高效管理。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受各种因素限制的性能下降，包括网络结构、处理器速度、缓冲/存储容量等。 2）开发关于操作员放置、数据存储、负载卸载和资源分配的分布式控制策略，以便实现高效的网络内信息处理。该项目将对实现未来数据密集型信息服务系统的可扩展性、稳健性和服务质量所需的基本设计原则和控制策略产生更深入和定量的理解。这些进步将通过跨越性能建模、网络、排队和优化等多个学科的协作努力来实现。