Seer：利用大数据应对日益复杂的云计算
Seer: Leveraging Big Data to Navigate the Increasing Complexity of Cloud Debugging
摘要：
云服务中的性能不可预测性会导致用户体验较差、可用性降低，并会影响收入。事后检测性能下降有助于系统采取纠正措施，但不能避免QoS违规。当一个服务由数十万个松散耦合的微服务组成时，事后检测QoS违规更加有害，因为性能打嗝可以快速传播到微服务的依赖图中。在这项工作中，我们专注于预测云环境中的QoS违规，以减轻性能的不可预测性。我们提出了Seer，这是一个云运行时，它利用云系统随着时间的推移收集的大量跟踪数据和一组实用的学习技术来发出即将发生的QoS违规的信号，并识别导致这些违规的微服务。一旦检测到即将发生的QoS违规，Seer使用机器级硬件事件来确定QoS违规的原因，并调整资源分配以防止它。在GCE上运行不同云微服务的10个40核服务器和200个实例集群的本地集群中，我们表明Seer正确预测QoS违规的时间为91%，并在89%的情况下将违规行为归因于正确的微服务。最后，Seer能够尽早检测到QoS违规，以便几乎总是成功地应用纠正措施。
英文摘要：
Performance unpredictability in cloud services leads to poor user experience, degraded availability, and has revenue ramifications. Detecting performance degradation a posteriori helps the system take corrective action, but does not avoid the QoS violations. Detecting QoS violations after the fact is even more detrimental when a service consists of hundreds of thousands of loosely-coupled microservices, since performance hiccups can quickly propagate across the dependency graph of microservices. In this work we focus on anticipating QoS violations in cloud settings to mitigate performance unpredictability to begin with. We propose Seer, a cloud runtime that leverages the massive amount of tracing data cloud systems collect over time and a set of practical learning techniques to signal upcoming QoS violations, as well as identify the microservice(s) causing them. Once an imminent QoS violation is detected Seer uses machine-level hardware events to determine the cause of the QoS violation, and adjusts the resource allocations to prevent it. In local clusters with 10 40-core servers and 200-instance clusters on GCE running diverse cloud microservices, we show that Seer correctly anticipates QoS violations 91% of the time, and attributes the violation to the correct microservice in 89% of cases. Finally, Seer detects QoS violations early enough for a corrective action to almost always be applied successfully.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
