The first talk will provide a broad overview of the field of telemetry-based analytics, including topics like monitoring frameworks, details about the nature of large-scale computing systems, and telemetry-based analytics frameworks. We will first describe some existing rule-based or heuristic analytics methods, and special emphasis will be given to opportunities for ML-based automated techniques.

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.

The third talk will focus on the frameworks that are able to leverage a limited number of labeled and a large amount of unlabeled data. The main motivation behind these frameworks is that supervised ML-based frameworks require large labeled data sets, and this requirement is restrictive for many real-world application domains. We will discuss two techniques in detail. The first technique requires only non-anomalous telemetry data to detect performance anomalies on compute nodes [5]. The second technique takes advantage of a few labeled anomalous samples to classify anomaly types [3]. We finally provide a glimpse towards the opportunities stemming from the combination of supervised and semi-supervised approaches [6].

The fourth talk will focus on operational data analytics solutions that provide runtime system insights for users and system administrators in real-world production systems [4, 5,8]. We will cover the challenges we faced while deploying ML-based solutions to production environments. We will also discuss the required system components and aspects to leverage ML-based approaches at scale and open problems.

In the hands-on section, we will present (a subset) of data collected from a Tier-0 supercomputing system in CINECA (Marconi 100). On that subset of data, we will demonstrate a semi-supervised anomaly detection approach [6].