NGS: Performance Mining of Large-Scale Data-Intensive Distributed Object Applications

NGS：大规模数据密集型分布式对象应用程序的性能挖掘

• 批准号: 0103708
• 负责人: Mohammed Zaki
• 金额: $ 40.95万
• 依托单位: Rensselaer Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-09-15 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0103708&HistoricalAwards=false
• 关键词: NGS; Performance; Mining; Large; Scale

EIA-0103708Mohammed J. ZakiRensselaer Polytechnic InstituteNGS Performance Mining of large-scale Data-Intensive Distributed Object ApplicationsThe objective of the proposal is to develop a performance measurements-based run-time environment for supporting large data-intensive distributed object applications. The system will provide continuous and adaptive performance optimization via a combination of performance data mining, critical path discovery and speculative execution.To address these challenges for next generation software systems we propose to develop the PERFMINER engine for the performance mining. PERFMINER, a system for continuous performance optimization via mining, will enable a distributed object system to: 1) discover its own critical path, 2) detect new opportunities for speculative processing, and 3) to facilitate modifying an object's behaviors (i.e., methods) at run-time in response to newly acquired knowledge.

mohammed J. ZakiRensselaer理工学院大规模数据密集型分布式对象应用程序的性能挖掘提案的目标是开发基于性能测量的运行时环境，以支持大型数据密集型分布式对象应用程序。该系统将通过性能数据挖掘、关键路径发现和推测执行相结合，提供持续和自适应的性能优化。为了解决下一代软件系统面临的这些挑战，我们建议开发用于性能挖掘的PERFMINER引擎。PERFMINER是一个通过挖掘进行持续性能优化的系统，它将使分布式对象系统能够：1)发现自己的关键路径，2)检测推测处理的新机会，以及3)在运行时根据新获得的知识修改对象的行为（即方法）。