  Anomaly detection is an important step in the management and monitoring of
data centers and cloud computing platforms. The ability to detect anomalous
virtual machines before real failures occur results in reduced downtime while
operations engineers urgently recover malfunctioning virtual machines,
efficient root cause analysis, and improved customer optics in the event said
malfunction lead to an outage. Virtual machines could fail at any time, whether
in a lab or production system. If there is no anomaly detection system, and a
virtual machine in a lab environment fails, the QA and DEV team will have to
switch to another environment while the OPS team fixes the failure. The
potential impact of failing to detect anomalous virtual machines can result in
financial ramifications, both when developing new features and servicing
existing ones. This paper presents a model that can efficiently detect
anomalous virtual machines both in production and testing environments.
