Toward Automatic Failure Diagnosis in the Cloud

迈向云端自动故障诊断

• 批准号: 435805-2013
• 负责人: Yuan, Ding
• 金额: $ 1.46万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=574183
• 关键词: Toward; Automatic; Failure; Diagnosis; Cloud

As software systems have grown in size, complexity, and cost, it has become increasingly difficult to deliver bullet-proof software, resulting in many software failures in the production environment. This unfortunate fact is further exacerbated by the trend of cloud-computing - where it is even harder to make systems failure-free as they become increasingly more complex and distributed. Once a failure occurs in these production systems (i.e., users cannot receive expected service), vendors need to trouble-shoot it as quickly as possible since every minute of the downtime is costly. For example, it is estimated that Amazon will lose 2 million dollars for every hour of its downtime. Such downtime will be even more catastrophic for those mission-critical software services. For example, the 2003 northeast blackout, caused by a software bug, resulted in over 10 million people in Ontario and 45 million in U.S. out of power for at least 7 hours. Later an investigation report attributes "failure to provide effective real-time diagnostic support" as one of the main reasons behind such great damage. Finally, a prolonged trouble-shooting process will frustrate the users and significantly erode the vendor's reputation. The goal of this proposed research is to expedite the trouble-shooting of failures in cloud-based distributed systems. In particular, my research consists three progressive thrusts that together, will automate the diagnosis of such failures, and significantly reduce the downtime of the cloud-based distributed systems.

随着软件系统在规模、复杂性和成本上的增长，交付防弹软件变得越来越困难，导致生产环境中的许多软件故障。云计算的趋势进一步加剧了这一不幸的事实--在云计算中，随着系统变得越来越复杂和分布式，使系统无故障变得更加困难。 一旦这些生产系统中发生故障（即，用户无法获得预期的服务），供应商需要尽快排除故障，因为停机时间的每一分钟都是昂贵的。例如，据估计，亚马逊每停机一小时将损失200万美元。对于那些关键任务软件服务来说，这种停机时间将更具灾难性。例如，2003年东北部停电，由软件错误引起，导致安大略超过1000万人和美国4500万人断电至少7小时。后来的一份调查报告将“未能提供有效的实时诊断支持”列为造成如此巨大损害的主要原因之一。最后，长时间的故障排除过程将使用户感到沮丧，并严重损害供应商的声誉。 这项研究的目标是加快基于云的分布式系统故障的故障排除。特别是，我的研究包括三个渐进的推力，它们将自动诊断此类故障，并显着减少基于云的分布式系统的停机时间。