Toward Automatic Failure Diagnosis in the Cloud

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-随着系统变得越来越复杂和分布，使系统无故障变得更加困难。一旦这些生产系统发生故障(即用户无法获得预期的服务)，供应商需要尽快排除故障，因为停机的每一分钟都是代价高昂的。例如，据估计，亚马逊每宕机一小时就会损失200万美元。这样的停机将对那些任务关键型软件服务造成更大的灾难。例如，2003年由软件漏洞引起的东北大停电，导致安大略省超过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”列为造成如此巨大破坏的主要原因之一。最后，长时间的故障排除过程会让用户感到沮丧，并严重侵蚀供应商的声誉。本研究的目标是加快基于云的分布式系统故障的故障排除。具体地说，我的研究包括三个渐进的推力，它们一起将自动诊断此类故障，并显著减少基于云的分布式系统的停机时间。

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.