COLLABORATIVE RESEARCH: Data Mining Meets I/O Performance Evaluation: Advanced Statistical Tools for Analyzing Bursty Traffic

协作研究：数据挖掘与 I/O 性能评估的结合：用于分析突发流量的高级统计工具

• 批准号: 0083130
• 负责人: Tara Madhyastha
• 金额: $ 20.13万
• 依托单位: University of California-Santa Cruz
• 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: 2001-09-01 至 2005-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0083130&HistoricalAwards=false
• 关键词: COLLABORATIVE; RESEARCH; Data; Mining; Meets

The goal of this collaborative research project involving Christos Faloutsos and Ngai Hang Chang at Carnegie Mellon (award 0083148) and Tara Madhyastha at U of Cal Santa Cruz (award 0083130) is to develop and apply statistical and datamining tools to analyze bursty time sequences, with emphasis on I/O traffic optimization. The interdisciplinary team includes researchers in computer science, computer engineering and statistics, and industry collaborators. The approach has three parts: (1) advanced statistical tools using the ``ARFIMA'' method; (2) wavelets and the related ``80-20 law'' to model disk traffic; and (3) incorporation of these modelsinside the so-called ``Active Disks'', with the goal to build self-tuning, adaptive disk subsystems.The results will advance data mining and statistics as well as disk design. An easy-to-use toolkit "T-REX" will aid in I/O and systems design, handling bursty traffic, and better buffering and prefetching. The theory behind the T-REX toolkit will be based on new data mining algorithms and statistical methods that model self-similar time sequences (like web and network traffic,in addition to I/O traffic). The research team has strong ties with database, data mining and disk manufacturing industrial groups, and this will aid in testing the research toolkit and its technology transfer. It can be expected that the T-REX system will significantly aid the design of disk sub-systems with beneficial impact on the storage industry.

这个合作研究项目涉及卡内基梅隆大学的Christiane Faloutsos和Ngai Hang Chang（获奖0083148）以及加州大学圣克鲁斯的塔拉Madhyastha（获奖0083130），其目标是开发和应用统计和数据挖掘工具来分析突发时间序列，重点是I/O流量优化。跨学科团队包括计算机科学，计算机工程和统计学的研究人员以及行业合作者。该方法有三个部分：（1）使用"ARFIMA“方法的先进统计工具;（2）小波和相关的" 80-20定律”来模拟磁盘流量;（3）将这些模型纳入所谓的“活动磁盘”，目的是建立自调整、自适应的磁盘子系统。一个易于使用的工具包“T-REX”将有助于I/O和系统设计，处理突发流量，以及更好的缓冲和预取。T-REX工具包背后的理论将基于新的数据挖掘算法和统计方法，这些算法和方法对自相似时间序列（如Web和网络流量，以及I/O流量）进行建模。研究团队与数据库、数据挖掘和磁盘制造工业集团有着密切的联系，这将有助于测试研究工具包及其技术转让。可以预期，T-REX系统将大大有助于磁盘子系统的设计，并对存储行业产生有益的影响。