DC: Small: Collaborative Research: DARE: Declarative and Scalable Recovery

DC：小型：协作研究：DARE：声明式和可扩展的恢复

• 批准号: 1016924
• 负责人: Haryadi Gunawi
• 金额: --
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-15 至 2013-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1016924&HistoricalAwards=false
• 关键词: DC; Small; Collaborative; Research; DARE; DC; Small; Collaborative; Research; DARE

One dominant characteristic of today's large-scale computing systemsis the prevalence of large storage clusters. Storage clusters at thescale of hundreds or thousands of commodity machines areincreasingly being deployed. At companies like Amazon, Google, Yahoo,and others, thousands of nodes are managed as a single system.As large clusters have brought many benefits, they also bring a newchallenge: a growing number and frequency of failures that must bemanaged. Bits, sectors, disks, machines, racks, and many othercomponents fail. With millions of servers and hundreds of datacenters, there are millions of opportunities for these components tofail. Failing to deal with failures will directly impact thereliability and availability of data and jobs.Unfortunately, we still hear data-loss stories even recently. Forexample, in March 2009, Facebook lost millions of photos due tosimultaneous disk failures that "should" rarely happen at the sametime (but it happened); in July 2009, a large bank was fined a recordtotal of 3 millions pounds after losing data on thousands of itscustomers; more recently, in October 2009, T-Mobile Sidekick, whichuses Microsoft's cloud service, also lost its customer data. Theseincidents have shown that existing large-scale storage systems arestill fragile to failures.To address the challenges of large-scale recovery, the goal of thisproject is to: (1) seek the fundamental problems of recovery intoday's scalable world of computing, (2) improve the reliability,performance, and scalability of existing large-scale recovery, and (3)explore formally grounded languages to empower rigorous specificationof recovery properties and behaviors. Our vision is to build systemsthat "DARE to fail": systems that deliberately fail themselves,exercise recovery routinely, and enable easy and correct deployment ofnew recovery policies.For more information, please visit this website:http://boom.cs.berkeley.edu/dare/

当今大规模计算系统的主要特征是大型存储簇的流行。 数百或数千台商品机器的储存簇正在部署。在亚马逊，Google，Yahoo和其他公司等公司中，成千上万的节点被管理为一个系统。正如大型集群带来了许多好处，它们还带来了纽卡勒格：越来越多的失败的频率和频率，必须遭受beman的失败。位，部门，磁盘，机器，机架和许多其他组件失败。 拥有数百万台服务器和数百个数据中心，这些组件有数百万个托尾的机会。未能处理故障将直接影响数据和工作的可用性和可用性。例如，在2009年3月，Facebook丢失了数百万张照片，原因是“应该”在Sametime上很少发生“应该”发生的磁盘故障（但事件发生了）； 2009年7月，一家大型银行因损失了数千个其客户的数据而被罚款300万英镑。最近，在2009年10月，T-Mobile Sidekick是Microsoft的Cloud Service，也丢失了其客户数据。 这些因素表明，现有的大规模存储系统artill易碎失败。为了解决大规模恢复的挑战，该项目的目标是：（1）寻求恢复量的计算机的基本问题，（2）提高现有大规模恢复的可靠性，绩效和伸缩性的属性，并探索了形式上恢复的属性，并恢复了形式上的态度，并恢复了形式上的语言，以恢复范围。 我们的愿景是构建“敢于失败”的系统：故意失败，经常行使恢复并启用新恢复策略的系统。有关更多信息，请访问此网站：http：//boom.cs.berkeley.edu/dare/dare/dare/