SOFTWARE:"Cluster-based Runtime Support for Data-Intensive Online Applications"

软件：“数据密集型在线应用程序基于集群的运行时支持”

• 批准号: 0234346
• 负责人: Tao Yang
• 金额: --
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-03-15 至 2008-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0234346&HistoricalAwards=false
• 关键词: SOFTWARE; Cluster; based; Runtime; Support

Data-intensive applications such as data mining demand significant compute and I/O resources. With the ubiquity of World Wide Web and the advances in clustering technologies, it becomes a reachable goal to bring these data-intensive applications to a large number of users in real-time. Online applications such as Internet services differ from their offline counterpart in the performance sensitivity to highly concurrent workload and the availability requirement. It is challenging to achieve high performance, reliability, and efficient resource management for these applications under highly concurrent workload.This project addresses the system and programming-level issues in buildinghigh performance and reliable runtime support for highly concurrentnetwork services and online applications. There are three main researchthrusts in this project. First, cluster-based system architectures are studied with a focus on overall system scalability/availability as well as load balancing and data replication support. Secondly, this project investigates programming and runtime support to simplify the development and deployment of online applications that involve concurrent and parallel data manipulation on clusters. Thirdly, a cluster-based resource management framework will be developed with the combined goal of quality-of-service and overall resourceutilization efficiency. Throughout this project, efforts will be made toexamine data-intensive applications and validate the effectiveness of theproposed work. Additional issues will be investigated as more experienceare gained through system development and application evaluation.The developed software will be released in public for potential use of otherapplications.Since more and more data-intensive services are being madeavailable through intranets and the Internet, it is expected that thedeveloped results will have broad impacts. Research described in this proposal will directly benefit instruction improvement for undergraduate and graduate education at PIs' institutions and enhance students' experience in cluster-basedcomputing.

数据挖掘等数据密集型应用程序需要大量的计算和I/O资源。随着万维网的无处不在和集群技术的进步，将这些数据密集型应用实时提供给大量用户成为一个可以实现的目标。Internet服务等在线应用程序与离线应用程序的不同之处在于，它们对高并发工作负载和可用性要求的性能敏感性不同。在高并发工作负载下实现高性能、高可靠性和高效率的资源管理是一项具有挑战性的工作。该项目解决了在为高并发网络服务和在线应用构建高性能和可靠的运行时支持方面的系统和编程级别的问题。该项目有三个主要研究方向。首先，研究了基于集群的系统体系结构，重点研究了总体系统可伸缩性/可用性以及负载平衡和数据复制支持。其次，该项目研究编程和运行时支持，以简化在线应用程序的开发和部署，这些应用程序涉及集群上的并发和并行数据操作。第三，将制定一个以服务质量和总体资源利用效率为综合目标的基于集群的资源管理框架。在整个项目中，将努力审查数据密集型应用程序，并验证拟议工作的有效性。随着通过系统开发和应用评估获得更多经验，还将调查其他问题。开发的软件将公开发布，供其他应用程序使用。由于越来越多的数据密集型服务通过内联网和互联网提供，预计开发的结果将产生广泛影响。这项建议中描述的研究将直接有益于个人投资机构本科生和研究生教育的教学改进，并增强学生在基于集群的计算方面的经验。