CAREER: Improving Storage System Performance, Dependability and Manageability Using System Mining Techniques

职业：使用系统挖掘技术提高存储系统性能、可靠性和可管理性

• 批准号: 0347854
• 负责人: Yuanyuan Zhou
• 金额: $ 44.94万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-06-01 至 2010-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0347854&HistoricalAwards=false
• 关键词: CAREER; Improving; Storage; System; Performance

Technology trends indicate that today's computing is becoming more and more data-centric. The widespread use of devices and services has created unprecedented demand to store and retrieve information. The current annual growth of storage demand is 60%. By 2008, the average data center will manage 10 times as much data as it does today. According to a recent study conducted by UC Berkeley, the annual storage demand is roughly 1.5 exabytes of storage, around 250 megabytes per person for everyone on earth.To satisfy the increasing data service demand, modern storage systems need to address three challenges: (1) performance, delivering satisfactory performance to keep up with the rapid growing processor speed; (2) dependability, providing reliability and availability to minimize data access loss, which currently costs companies more that $250,000/hour and one-third to one-half of a company's total IT budget; (3) manageability, simplifying storage administrator's jobs to reduce the storage maintenance cost, which is currently almost nine times the storage equipment purchase price.This proposal addresses these three challenges. It investigates a novel technology called system mining that applies data mining techniques to storage systems to improve their performance, dependability and manageability. More specifically, the proposed system hinges on the following innovations:1) Performance: using frequent sequence mining, clustering, classification and other data mining algorithms to characterize storage access patterns and infer data semantics for guiding storage cache management, prefetching, disk scheduling, and data layout to maximize storage performance;2) Dependability: applying outlier analysis, signature analysis and other data mining techniques to unified, correlated activity logs to detect and correct storage administrators' mistakes and other human errors;Manageability: building a context-aware, "self-maturing" autonomic storage system that can learn from storage administrators and automatically generate administrative scripts to gradually minimize administrators' involvement.

技术趋势表明，当今的计算正变得越来越以数据为中心。 设备和服务的广泛使用产生了前所未有的存储和检索信息的需求。 目前存储需求的年增长率为60%。 到2008年，平均每个数据中心管理的数据量将是现在的10倍。 根据加州大学伯克利分校最近进行的一项研究，每年的存储需求约为1.5艾字节，地球上每个人的存储需求约为250兆字节。为了满足不断增长的数据服务需求，现代存储系统需要解决三个挑战：（1）性能，提供令人满意的性能以跟上快速增长的处理器速度;（2）可靠性，提供可靠性和可用性，以最大限度地减少数据访问损失，目前公司的成本超过250，000美元/小时，占公司总IT预算的三分之一到二分之一;（3）简化存储管理员的工作，降低存储维护成本，这几乎是目前存储设备购买价格的九倍。本提案解决了这三个挑战。 系统挖掘是一种将数据挖掘技术应用于存储系统以提高存储系统的性能、可靠性和可扩展性的新技术。 更具体地说，所提出的系统取决于以下创新：1）性能：使用频繁序列挖掘、聚类、分类和其他数据挖掘算法来表征存储访问模式并推断数据语义，以指导存储高速缓存管理、预取、磁盘调度和数据布局，从而最大化存储性能;2）可靠性：将离群值分析、签名分析和其他数据挖掘技术应用于统一、相关的活动日志，以检测和纠正存储管理员的错误和其他人为错误;管理：构建一个可感知环境的“自我成熟”自主存储系统，该系统可以向存储管理员学习，并自动生成管理脚本，以逐步减少管理员的参与。