  The stability and performance of Cloud platforms are essential as they
directly impact customers' satisfaction. Cloud service providers use Cloud
monitoring tools to ensure that rendered services match the quality of service
requirements indicated in established contracts such as service-level
agreements. Given the enormous number of resources that need to be monitored,
highly scalable and capable monitoring tools are designed and implemented by
Cloud service providers such as Amazon, Google, IBM, and Microsoft. Cloud
monitoring tools monitor millions of virtual and physical resources and
continuously generate logs for each one of them. Considering that logs magnify
any technical issue, they can be used for disaster detection, prevention, and
recovery. However, logs are useless if they are not assessed and analyzed
promptly. Thus, we argue that the scale of Cloud-generated logs makes it
impossible for DevOps teams to analyze them effectively. This implies that one
needs to automate the process of monitoring and analysis (e.g., using machine
learning and artificial intelligence). If the automation will witness an
anomaly in the logs --- it will alert DevOps staff. The automatic anomaly
detectors require a reliable and scalable platform for gathering, filtering,
and transforming the logs, executing the detector models, and sending out the
alerts to the DevOps staff. In this work, we report on implementing a prototype
of such a platform based on the 7-layered architecture pattern, which leverages
micro-service principles to distribute tasks among highly scalable,
resources-efficient modules. The modules interact with each other via an
instance of the Publish-Subscribe architectural pattern. The platform is
deployed on the IBM Cloud service infrastructure and is used to detect
anomalies in logs emitted by the IBM Cloud services, hence the dogfooding.
