Identifying Changed or Sick Resources from Logs

The identification of important changes in a complex distributed system is a challenging data science problem. Solving this problem is critical for tools for managing modern cloud infrastructure stacks and other large complex distributed systems. In this paper, we investigate two specific approaches to using log data to solve this problem. The first approach is comparing a source's current and past behavior. Some solutions that perform anomaly detection on numeric data from the data center are inevitably relying on global change point detection concepts. On the other hand, while log data promises a significantly different perspectives and dimensions to accomplish a similar task, state-of-the-art of solutions lack a capability to automatically detect significant change points in the log stream of an event source through learning its behavioral patterns. Such change points indicate the most important times when the source's behavior significantly differs from the past. A second complementary approach to real-time change detection involves comparing a source's current behavior with the current behavior of its peers in a population of sources serving a common role in the data center. Employing the concept of event types of log messages introduced earlier, we propose algorithms for each of these approaches that apply classical statistical and machine learning techniques to data capturing the distribution of those constructs. We demonstrate experimental results from our prototype algorithms.