The second talk will focus on anomaly and application detection in large-scale computing systems. We will cover the motivation behind detecting performance anomalies in large-scale computing systems and summarize existing synthetic performance anomaly suites that can help generate labeled anomalous data samples for supervised methods [9]. Next, we will discuss several successful supervised anomaly detection/diagnosis methods introduced in the last five years [1, 7]. We will also cover the security aspect and provide an example from a framework that can identify running applications, especially important for detecting unwanted applications such as cryptocurrency miners and password crackers [2]. We will conclude with the unique strengths and limitations of the supervised methods, as well as the usability of these methods in production systems.