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

当今大规模计算系统的一个主要特征是大存储集群的流行。 数百或数千台商用机器规模的存储集群正越来越多地被部署。在亚马逊、谷歌、雅虎和其他公司，成千上万的节点作为一个单一的系统进行管理。在大型集群带来许多好处的同时，它们也带来了新的挑战：必须管理的故障数量和频率不断增加。位、扇区、磁盘、机器、机架和许多其他组件出现故障。 由于有数百万台服务器和数百个数据中心，因此这些组件有数百万次发生故障的机会。失败的处理将直接影响数据和工作的可靠性和可用性。不幸的是，即使在最近，我们仍然听到数据丢失的故事。例如，在2009年3月，Facebook由于同时发生的磁盘故障而丢失了数百万张照片，这些故障“应该”很少同时发生（但它发生了）;在2009年7月，一家大型银行在丢失数千名客户的数据后被处以创纪录的300万英镑罚款;最近，在2009年10月，使用微软云服务的T-Mobile Sidekick也丢失了客户数据。 这些事件表明，现有的大规模存储系统仍然很容易发生故障。为了应对大规模恢复的挑战，本项目的目标是：（1）在当今可伸缩的计算世界中寻找恢复的基本问题，（2）提高现有大规模恢复的可靠性、性能和可伸缩性，和（3）探索正式接地语言，以授权严格的规格说明的恢复属性和行为。 我们的愿景是构建“敢于失败”的系统：系统故意使自己失败，定期进行恢复，并能够轻松正确地部署新的恢复策略。有关更多信息，请访问此网站：http://boom.cs.berkeley.edu/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/