  Cloud systems have become increasingly popular in recent years due to their
flexibility and scalability. Each time cloud computing applications and
services hosted on the cloud are affected by a cloud outage, users can
experience slow response times, connection issues or total service disruption,
resulting in a significant negative business impact. Outages are usually
comprised of several concurring events/source causes, and therefore
understanding the context of outages is a very challenging yet crucial first
step toward mitigating and resolving outages. In current practice, on-call
engineers with in-depth domain knowledge, have to manually assess and summarize
outages when they happen, which is time-consuming and labor-intensive. In this
paper, we first present a large-scale empirical study investigating the way
on-call engineers currently deal with cloud outages at Microsoft, and then
present and empirically validate a novel approach (dubbed Oasis) to help the
engineers in this task. Oasis is able to automatically assess the impact scope
of outages as well as to produce human-readable summarization. Specifically,
Oasis first assesses the impact scope of an outage by aggregating relevant
incidents via multiple techniques. Then, it generates a human-readable summary
by leveraging fine-tuned large language models like GPT-3.x. The impact
assessment component of Oasis was introduced in Microsoft over three years ago,
and it is now widely adopted, while the outage summarization component has been
recently introduced, and in this article we present the results of an empirical
evaluation we carried out on 18 real-world cloud systems as well as a
human-based evaluation with outage owners. The results show that Oasis can
effectively and efficiently summarize outages, and lead Microsoft to deploy its
first prototype which is currently under experimental adoption by some of the
incident teams.
